Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification (2201.01380v1)

Abstract: The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introduce a new method based on Linear Programming for matching clusters of coronal holes. The final matching is then performed using Random Forests. The methods were carefully validated using consensus maps derived from multiple readers, manual clustering, manual map classification, and method validation for 50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN by providing accurate boundary detection. Overall, the method gave a 95.5% map classification accuracy.

• The paper introduces a novel multimodal image processing pipeline that integrates multiple segmentation methods with physical constraints to improve coronal hole detection.
• It employs Henney-Harvey, SegNet, and FCN initialization, refined by a level-set method and Random Forests, achieving a 95.5% map classification accuracy.
• The research provides actionable insights for advanced geomagnetic storm forecasting by addressing the challenges of cluster matching and physical map classification.

Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification

Introduction

The paper "Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification" introduces a comprehensive suite of image processing techniques designed to improve the prediction of geomagnetic storms by accurately modeling and forecasting solar activities, specifically coronal holes. The research is divided into three subproblems: segmentation of coronal holes based on physical constraints, matching clusters of coronal holes between different maps, and physical map classification.

Figure 1: The coronal hole segmentation problem. The figure illustrates the manual segmentation process where the coronal holes appear as dark regions in the Extreme ultraviolet (EUV) images. Black regions represent solar regions for which we do not have any observations.

Methodology

Coronal Hole Segmentation

The segmentation approach employs a multi-modal method that integrates three distinct segmentation techniques, each operating at different resolution scales, to initialize a level-set method. This level-set method then refines the segmentation to align with the magnetic boundaries, improving precision. The segmentation is initialized using the Henney-Harvey algorithm, SegNet, and Fully Convolutional Networks (FCN). These initial segmentations are processed using Random Forests to enforce physical constraints such as unipolarity and low EUV brightness.

Cluster Matching

To match coronal hole clusters between observed images and model-generated maps, the paper introduces a new method utilizing Linear Programming. Clustering is based on physical distances calculated on a spherical surface, taking into account the geometry and potential observational gaps (Figure 2). The matching algorithm identifies corresponding clusters, detects newly generated clusters in the model maps that do not appear in observations, and vice versa, thereby ensuring high accuracy in cluster correlation.

Figure 2: Coronal hole cluster matching example (02-04-2011). Matched clusters share the same color.

Map Classification

The final step involves classifying physical maps to determine their suitability for forecasting geomagnetic storms. This classification is based on the clustering and matching output, where maps are judged on their alignment with observation-derived consensus maps. A Random Forest classifier is utilized to categorize maps into "good" or "bad" matches for forecasting, achieving high classification accuracy due to the ensemble learning technique.

Results

The proposed multimodal segmentation method significantly outperforms traditional methods like SegNet and U-net, achieving a 95.5% map classification accuracy. The research highlights the limitations of deep learning-based approaches in resolution-limited contexts and provides an effective solution through a novel combination of neural network initialization and level-set refinement.

Discussion

The research presents an effective framework for enhancing the accuracy of solar wind and magnetic storm predictions. By systematically addressing segmentation and matching challenges through a combination of automated methods and physical constraints, the paper lays a robust foundation for improving solar weather models. Future developments can extend this work by incorporating real-time data processing and leveraging advanced machine learning models to further refine predictions.

Conclusion

The paper successfully demonstrates a comprehensive approach to coronal hole segmentation and model evaluation, setting a new standard in solar image processing for geomagnetic forecasting. The integration of different segmentation methods with physical boundary conditions offers a valuable contribution to space weather prediction, establishing a reliable framework that can adapt to evolving solar observation technologies.