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

TL;DR

This paper develops and validates advanced image processing techniques for coronal hole segmentation, matching, and classification to improve physical model selection and geomagnetic storm prediction.

Contribution

It introduces a multi-modal segmentation approach, a Linear Programming matching method, and a Random Forest classification for coronal holes, outperforming existing methods.

Findings

Multi-modal segmentation outperforms SegNet, U-net, Henney-Harvey, and FCN.

Achieved 95.5% accuracy in coronal map classification.

Validated methods using consensus maps, manual clustering, and classification.

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.