  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.
