A Novel Method of Unsupervised Change Detection Using Multi-Temporal PolSAR Images

Remote Sensing

Jie Yang¹  Jinqi Zhao¹  Le Yang¹  Wensong Liu¹ 

[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;

关键词: PolSAR;    unsupervised change detection;    test statistic;    generalized statistical region merging (GSRM);    generalized Gaussian mixture model (GMM);   

DOI  :  10.3390/rs9111135

来源: DOAJ

【 摘 要 】

The existing unsupervised change detection methods using full-polarimetric synthetic aperture radar (PolSAR) do not use all the polarimetric information, and the results are subject to the influence of noise. In order to solve these problems, a novel automatic and unsupervised change detection approach based on multi-temporal full PolSAR images is presented in this paper. The proposed method integrates the advantages of the test statistic, generalized statistical region merging (GSRM), and generalized Gaussian mixture model (GMM) techniques. It involves three main steps: (1) the difference image (DI) is obtained by the likelihood-ratio parameter based on a test statistic; (2) the GSRM method is applied to the DI; and (3) the DI, after segmentation, is automatically analyzed by the generalized GMM to generate the change detection map. The generalized GMM is derived under a non-Gaussian assumption for modeling the distributions of the changed and unchanged classes, and automatically identifies the optimal number of components. The efficiency of the proposed method is demonstrated with multi-temporal PolSAR images acquired by Radarsat-2 over the city of Wuhan in China. The experimental results show that the overall accuracy of the change detection results is improved and the false alarm rate reduced, when compared with some of the traditional change detection methods.

【 授权许可】

Unknown