When we consider the advance in spatial resolution of remote sensing images, there is a potential demand for spatial segmentation based on spatial pattern dynamics. In this paper, we propose a spatial pattern segmentation method as a spatial version of adaptive filtering and change detection of temporal process. Our prosal detects boundaries between adjacent spatial clusters by evaluating the predictability of adaptive filters. Preliminary experiments show significant validity for the boundary detection. In addition, we propose a critical change detection method as temporal version of our spatial segmentation method which evaluates predictablility based on transition probability matrix. An effective critical change detection is realized by the combination of the evaluation of (1) the transition probability from a normal state to singular state; and (2) the stability of singular state.
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