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

• URL: http://arxiv.org/abs/2201.01380v1
• Date: Tue, 4 Jan 2022 23:19:53 GMT
• Title: Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification
• Authors: V. Jatla, M.S. Pattichis, and C.N. Arge
• Abstract summary: 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. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN by providing accurate boundary detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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