Bulletin of Surveying and Mapping ›› 2023, Vol. 0 ›› Issue (6): 88-92.doi: 10.13474/j.cnki.11-2246.2023.0173

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An improved fuzzy Wishart-PSO polarimetric SAR image intelligent clustering algorithm

ZHU Teng1,2, GAO Zhaozhong1,2, SHEN Chen1, HUANG Tielan1, ZHOU Huiyuan3   

  1. 1. School of Surveying and Remote Sensing Information, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China;
    2. Virtual Simulation Training Base of Surveying and Mapping Geographic Information Technology, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China;
    3. Guangzhou South Surveying and Mapping Technology Co., Ltd., Guangzhou 510000, China
  • Received:2022-08-08 Published:2023-07-05

Abstract: Aiming at the problems of low accuracy of polarized SAR image clustering, large data volume of polarization parameters and complicated calculation, this paper proposes an intelligent clustering method for particle swarm of PolSAR images based on improved fuzzy Wishart distance. The method improves the traditional Wishart clustering evaluation criterion by combining fuzzy division for PolSAR data distribution to reduce the influence of isolated point noise, then completes the initial division of clusters according to the polarization scattering mechanism, and finally introduces the particle swarm optimization framework in the iterative optimization search step to improve the effectiveness of clustering centers and classification accuracy. In the experimental part, the effectiveness of the fuzzy Wishart-PSO clustering algorithm is verified by using L-band AIRSAR data and X-band high-resolution polarized SAR data respectively, and the classification results are significantly more reasonable than the traditional H/α-Wishart method, and the clustering accuracy can reach 90%.

Key words: particle swarm optimization algorithm, fuzzy set, polarimetric SAR, unsupervised classification, Wishart distance

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