Unsupervised segmentation of weather images into features that correspond to physical storms is a fundamental and difficult problem. Treating an infrared satellite image as a Markov random field, the Kolmogorov-Smirnov distance between the local distribution of spatial statistics and the global statistics of classified regions is used to segment the image using a relaxation algorithm. An outlier class is utilized to capture as yet unclassified pixels. We demonstrate the results of different initialization methods on the final segmentation and point out where the method is deficient.
['Valliappa Lakshmanan', 'Victor E. DeBrunner', 'Robert M. Rabin']
Texture-based segmentation of satellite weather imagery