A Novel Pol SAR Image Classification Method with Subsequent Category Adjustment by Terrain Scattering Characteristic
LIU Li-min1, YU Jie2, 3, 1*, WANG Yan-bing2，3, CHEN Mi2, 3, ZHU Teng1, YE Qiu-hong2
1. School of Remote Sensing and Information Engineering，Wuhan University，Wuhan 430079，China 2. College of Resource Environment and Tourism，Capital Normal University，Beijing 100048，China 3. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation，Capital Normal University，Beijing 100048，China
Abstract：The present paper，on the basis of analyzing the terrain scattering characteristics，proposed a novel supervised classification method combined with complex Wishart classifier ideology. This method used coherent matrix which almost contains all the polarization information to make subsequent adjustments for the supervised classification result to achieve higher accuracy division categories. For the first beginning, supervised classification was carried out on the Cloude & Pottier polarimetric characteristics combination to get the initial classification result．Then, in order to achieve the purpose to correct the mistakes resulting from just using the spatial distribution of feature vectors in supervised classification, we did some analysis as follow. The accuracy analysis of the classification results and the analysis of study area feature scattering similarity play an important role in our study to help us make the determination that the pixels need to be adjusted. Furthermore, taking the mean value of each category coherence matrix as the initial cluster centers of subsequent iterations, and using Kernel Fuzzy C-Means algorithm to adjust the fixed pixel set categories by subsequent category iterative correction, the fine and high-accuracy classification results were obtained, combined with complex Wishart distribution of coherence matrix. The domestic X-band full polarization SAR data of Lingshui area in Hainan province was applied in this classification experiment. The experiment results demonstrate that the proposed method can obtain a favorable classification accuracy polarization SAR image classification results, and better meet the scattering characteristics of the surface objects compared to the original method.
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