Mixture of Spectral Generative Adversarial Networks for Imbalanced Hyperspectral Image Classification (2009.13037v1)

Abstract: We propose a three-player spectral generative adversarial network (GAN) architecture to afford GAN with the ability to manage minority classes under imbalance conditions. A class-dependent mixture generator spectral GAN (MGSGAN) has been developed to force generated samples remain within the domain of the actual distribution of the data. MGSGAN is able to generate minority classes even when the imbalance ratio of majority to minority classes is high. A classifier based on lower features is adopted with a sequential discriminator to form a three-player GAN game. The generator networks perform data augmentation to improve the classifier's performance. The proposed method has been validated through two hyperspectral images datasets and compared with state-of-the-art methods under two class-imbalance settings corresponding to real data distributions.