一种改进的模糊Wishart-PSO极化SAR影像智能聚类算法

doi:10.13474/j.cnki.11-2246.2023.0173

测绘通报 ›› 2023, Vol. 0 ›› Issue (6): 88-92.doi: 10.13474/j.cnki.11-2246.2023.0173

一种改进的模糊Wishart-PSO极化SAR影像智能聚类算法

朱腾^1,2, 高照忠^1,2, 申晨¹, 黄铁兰¹, 周惠苑³

1. 广东工贸职业技术学院测绘遥感信息学院, 广东广州 510510;
2. 广东工贸职业技术学院测绘地理信息技术虚拟仿真实训基地, 广东广州 510510;
3. 广州南方测绘科技股份有限公司, 广东广州 510000

收稿日期:2022-08-08 发布日期:2023-07-05
通讯作者: 申晨。E-mail:1541935325@qq.com
作者简介:朱腾(1989-),男,博士,讲师,现从事极化SAR影像解译方向研究工作。E-mail:jhgf_1234@sina.com
基金资助:
广东省普通高校青年创新人才类项目(2019GK QNCX020);广州市基础研究计划基础与应用基础研究项目 (20210201298);广东工贸职业技术学院高层次人才专项 (2021-gc-06);校级项目(2021-ZK-16)

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

ZHU Teng^1,2, GAO Zhaozhong^1,2, SHEN Chen¹, HUANG Tielan¹, ZHOU Huiyuan³

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

摘要/Abstract

摘要： 针对极化SAR影像聚类精度不高、极化参数数据量大、计算复杂的问题,本文提出了基于改进模糊Wishart距离的极化SAR影像粒子群智能聚类方法。该方法首先针对极化SAR数据分布,结合模糊划分改进传统Wishart聚类评价准则,减小孤立点噪声影响;然后根据极化散射机理完成聚类初始划分;最后在迭代寻优步骤引入粒子群优化框架,提高聚类中心有效性与分类精度。试验分别采用L波段AIRSAR数据及X波段高分辨率极化SAR数据验证了模糊Wishart-PSO聚类算法的有效性,分类结果较传统的H/α-Wishart方法合理性明显提高,聚类精度可达90%。

关键词: 粒子群优化算法, 模糊集, 极化SAR, 非监督分类, Wishart距离

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

中图分类号:

P237

朱腾, 高照忠, 申晨, 黄铁兰, 周惠苑. 一种改进的模糊Wishart-PSO极化SAR影像智能聚类算法[J]. 测绘通报, 2023, 0(6): 88-92.

ZHU Teng, GAO Zhaozhong, SHEN Chen, HUANG Tielan, ZHOU Huiyuan. An improved fuzzy Wishart-PSO polarimetric SAR image intelligent clustering algorithm[J]. Bulletin of Surveying and Mapping, 2023, 0(6): 88-92.

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一种改进的模糊Wishart-PSO极化SAR影像智能聚类算法

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

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