Unsupervised classification using polarimetric interferometric decomposition and Wishart classifier. Issue 8 (17th April 2020)

• Record Type: Journal Article
• Authors: Wang, Yu; Yu, Weidong
• Is Part Of: International journal of remote sensing. Volume 41:Issue 8(2020)
• Page Start: 2962
• Page End: 2985
• Publication Date: 2020-04-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1698074
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: In this paper, a decomposition scheme of the coherency matrix is presented to parse the information of polarimetric interferometric synthetic aperture radar (PolInSAR) images in detail. First, the decomposition method is improved by the polarimetric interferometric similarity parameter (PISP) to relief the overestimation occurred in the traditional four-component decomposition method. Second, after using the improved four-component decomposition results as the original inputs, the decomposition method is applied to retrieve scattering mechanisms or identify scatters, with the image separated into seven subsections. Finally, based on the modified decomposition results, the basic classification results are regarded as the feature training sets, and the Wishart classifier is then used as an optimized classification process. The applications of the decomposition and classification scheme are shown with typical representative L-band E-SAR images, which are used to show the robustness of the method, as well as with the first published airborne C-band PolInSAR data collected by the Institute of Electronics, Chinese Academy of Sciences, in November 2017. Experimental results demonstrate that the obtained decomposition and classification results are in good agreement with the actual physical scattering mechanisms.