REGION ADAPTIVE ADJUSTMENT STRATEGY BASED ON INFORMATION ENTROPY FOR REMOTE SENSING IMAGE SEGMENTATION
Keywords: Information Entropy, Coefficient of Variation, Boundary-fitting, Selection factor, Fuzzy Clustering, Image Segmentation
Abstract. For the difficulty of boundary-fitting in region-based algorithms, a region adaptive adjustment strategy based on information entropy is proposed for remote sensing image segmentation. Considering the characteristics of imperfect blocks that cover two homogeneous regions, a selection factor constructed by the spectral coefficient of variation and the information entropy of prior probability representing neighborhood constraint is designed to find the imperfect blocks. Then, the selected imperfect block is split into four equal parts, and new blocks enjoy the same membership as the original block. The model parameters are updated based on the current tessellation. If the fuzzy clustering objective function decrease, the split operation is certainly accepted, otherwise, it will be accepted with a certain probability to avoid local optimum. Finally, the experiments on simulated and multi-spectral remote sensing images show that the proposed strategy can accurately locate the imperfect blocks and effectively fit the boundary of homogeneous regions.