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Solar image analysis relies on the detection of coronal holes for predictingdisruptions to earth's magnetic field. The coronal holes act as sources ofsolar wind that can reach the earth. Thus, coronal holes are used in physicalmodels for predicting the evolution of solar wind and its potential forinterfering with the earth's magnetic field. Due to inherent uncertainties inthe physical models, there is a need for a classification system that can beused to select the physical models that best match the observed coronal holes.  The physical model classification problem is decomposed into threesubproblems. First, he thesis develops a method for coronal hole segmentation.Second, the thesis develops methods for matching coronal holes from differentmaps. Third, based on the matching results, the thesis develops a physical mapclassification system.  A level-set segmentation method is used for detecting coronal holes that areobserved in extreme ultra-violet images (EUVI) and magnetic field images. Forvalidating the segmentation approach, two independent manual segmentations werecombined to produce 46 consensus maps. Overall, the level-set segmentationapproach produces significant improvements over current approaches.  Physical map classification is based on coronal hole matching between thephysical maps and (i) the consensus maps (semi-automated), or (ii) thesegmented maps (fully-automated). Based on the matching results, the systemuses area differences,shortest distances between matched clusters, number andareas of new and missing coronal hole clusters to classify each map. Theresults indicate that the automated segmentation and classification systemperforms better than individual humans.

Automatic Segmentation of Coronal Holes in Solar Images and Solar Prediction Map Classification