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Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification (2402.10026v1)
Published 15 Feb 2024 in eess.IV and cs.CV
Abstract: Hyper spectral images have drawn the attention of the researchers for its complexity to classify. It has nonlinear relation between the materials and the spectral information provided by the HSI image. Deep learning methods have shown superiority in learning this nonlinearity in comparison to traditional machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great success for learning spatial and spectral features. However, it uses comparatively large number of parameters. Moreover, it is not effective to learn inter layer information. Hence, this paper proposes a neural network combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data sets. The results are compared with the state of-the-art deep learning-based models. This model performed better in all three datasets. It could achieve 99.83, 99.98 and 100 percent accuracy using only 30 percent trainable parameters of the state-of-art model in IP, PU and SA datasets respectively.
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Photodiagnosis and Photodynamic Therapy 33, 102165 (2021) Lu and Fei [2014] Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Sudharsan, S., Hemalatha, R., Radha, S.: A survey on hyperspectral imaging for mineral exploration using machine learning algorithms. In: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp. 206–212 (2019). IEEE Ortac and Ozcan [2021] Ortac, G., Ozcan, G.: Comparative study of hyperspectral image classification by multidimensional convolutional neural network approaches to improve accuracy. Expert Systems with Applications 182, 115280 (2021) ul Rehman and Qureshi [2021] Rehman, A., Qureshi, S.A.: A review of the medical hyperspectral imaging systems and unmixing algorithms’ in biological tissues. Photodiagnosis and Photodynamic Therapy 33, 102165 (2021) Lu and Fei [2014] Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ortac, G., Ozcan, G.: Comparative study of hyperspectral image classification by multidimensional convolutional neural network approaches to improve accuracy. Expert Systems with Applications 182, 115280 (2021) ul Rehman and Qureshi [2021] Rehman, A., Qureshi, S.A.: A review of the medical hyperspectral imaging systems and unmixing algorithms’ in biological tissues. Photodiagnosis and Photodynamic Therapy 33, 102165 (2021) Lu and Fei [2014] Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Rehman, A., Qureshi, S.A.: A review of the medical hyperspectral imaging systems and unmixing algorithms’ in biological tissues. Photodiagnosis and Photodynamic Therapy 33, 102165 (2021) Lu and Fei [2014] Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. 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IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. 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[2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. 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IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Lu, G., Fei, B.: Medical hyperspectral imaging: a review. Journal of biomedical optics 19(1), 010901–010901 (2014) Scholkopf and Smola [2018] Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ??? (2018) Bo et al. [2015] Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. 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[2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. 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IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. 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[2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. 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[2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. 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IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). 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[2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bo, C., Lu, H., Wang, D.: Hyperspectral image classification via jcr and svm models with decision fusion. IEEE Geoscience and Remote Sensing Letters 13(2), 177–181 (2015) Li et al. [2010] Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010) Li et al. [2011] Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. 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IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. 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IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. 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IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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[2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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[2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. 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IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2011) Du and Zhang [2010] Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. 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[2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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[2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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[2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. 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IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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[2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing 49(5), 1578–1589 (2010) Du and Zhang [2014] Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. 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[2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Du, B., Zhang, L.: Target detection based on a dynamic subspace. Pattern Recognition 47(1), 344–358 (2014) Bandos et al. [2009] Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. 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IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009) Prasad and Bruce [2008] Prasad, S., Bruce, L.M.: Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters 5(4), 625–629 (2008) Licciardi et al. [2011] Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9(3), 447–451 (2011) Villa et al. [2011] Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. 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IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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[2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing 49(12), 4865–4876 (2011) Krishnapuram et al. [2005] Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. 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[2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. 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IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. 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[2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. 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In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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[2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Krishnapuram, B., Carin, L., Figueiredo, M.A., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. 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IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. 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[2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. 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IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. 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[2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. 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[2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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IEEE transactions on pattern analysis and machine intelligence 27(6), 957–968 (2005) Wang et al. [2017] Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 14(11), 2077–2081 (2017) Fang et al. [2017] Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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[2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Fang, L., Wang, C., Li, S., Benediktsson, J.A.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement 66(7), 1646–1657 (2017) Guo et al. [2019] Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. 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[2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. 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[2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. 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IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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[2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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[2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. 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IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. 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IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Guo, Y., Yin, X., Zhao, X., Yang, D., Bai, Y.: Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking 2019(1), 1–9 (2019) Banerjee and Banik [2023] Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. 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[2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Banerjee, A., Banik, D.: Pooled hybrid-spectral for hyperspectral image classification. Multimedia Tools and Applications 82(7), 10887–10899 (2023) Xu et al. [2019] Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. 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[2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, M., Fang, H., Lv, P., Cui, L., Zhang, S., Zhou, B.: D-stc: Deep learning with spatio-temporal constraints for train drivers detection from videos. Pattern Recognition Letters 119, 222–228 (2019) Kang et al. [2018] Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Kang, X., Zhuo, B., Duan, P.: Dual-path network-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 16(3), 447–451 (2018) Yu et al. [2017] Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. 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IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. 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IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. 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[2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Yu, Y., Gong, Z., Wang, C., Zhong, P.: An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters 15(1), 23–27 (2017) Song et al. [2018] Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Song, W., Li, S., Fang, L., Lu, T.: Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing 56(6), 3173–3184 (2018) Cheng et al. [2018] Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. 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IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.: Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(11), 6712–6722 (2018) Chen et al. [2016] Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing 54(10), 6232–6251 (2016) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. 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IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. 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A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. 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IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Mou et al. [2017] Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(1), 391–406 (2017) Paoletti et al. [2018] Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. 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[2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Paoletti, M.E., Haut, J.M., Fernandez-Beltran, R., Plaza, J., Plaza, A.J., Pla, F.: Deep pyramidal residual networks for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57(2), 740–754 (2018) Roy et al. [2019] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. 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[2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 17(2), 277–281 (2019) Hu et al. [2020] Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. 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IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. 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IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. 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IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. 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IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Hu, W.-S., Li, H.-C., Pan, L., Li, W., Tao, R., Du, Q.: Spatial–spectral feature extraction via deep convlstm neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 58(6), 4237–4250 (2020) Zhou et al. [2019] Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. 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Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhou, F., Hang, R., Liu, Q., Yuan, X.: Hyperspectral image classification using spectral-spatial lstms. Neurocomputing 328, 39–47 (2019) https://doi.org/10.1016/j.neucom.2018.02.105 . Chinese Conference on Computer Vision 2017 Liu et al. [2017] Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional lstm based spectral-spatial feature learning for hyperspectral image classification. Remote Sensing 9(12), 1330 (2017) Xu et al. [2022] Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. 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IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. 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IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. 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IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
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IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Xu, Q., Yang, C., Tang, J., Luo, B.: Grouped bidirectional lstm network and multistage fusion convolutional transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 (2022) https://doi.org/10.1109/TGRS.2022.3207294 Zhang et al. [2022] Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Zhang, Z., Li, T., Tang, X., Hu, X., Peng, Y.: Caevt: Convolutional autoencoder meets lightweight vision transformer for hyperspectral image classification. Sensors 22(10), 3902 (2022) Vyawahare et al. [2022] Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Vyawahare, A., Tangsali, R., Mandke, A., Litake, O., Kadam, D.: Pict@ dravidianlangtech-acl2022: Neural machine translation on dravidian languages. arXiv preprint arXiv:2204.09098 (2022) Hochreiter et al. [2001] Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press In (2001) Greff et al. [2016] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems 28(10), 2222–2232 (2016) Van Houdt et al. [2020] Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artificial Intelligence Review 53(8), 5929–5955 (2020) Ji et al. [2012] Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35(1), 221–231 (2012) Zhong et al. [2017] Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral–spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Transactions on Geoscience and Remote Sensing 56(2), 847–858 (2017) Hamida et al. [2018] Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Hamida, A.B., Benoit, A., Lambert, P., Amar, C.B.: 3-d deep learning approach for remote sensing image classification. IEEE Transactions on geoscience and remote sensing 56(8), 4420–4434 (2018) Makantasis et al. [2015] Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE
- Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962 (2015). IEEE