Wishart distance‐based joint collaborative representation for polarimetric SAR image classification. Issue 11 (1st November 2017)

• Record Type: Journal Article
• Authors: Geng, Jie; Wang, Hongyu; Fan, Jianchao; Ma, Xiaorui; Wang, Bing
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 11(2017)
• Page Start: 1620
• Page End: 1628
• Publication Date: 2017-11-01
• Subjects: synthetic aperture radar -- radar imaging -- image classification -- radar polarimetry -- statistical analysis; polarimetric synthetic aperture radar -- PolSAR -- collaborative representation classifier -- polarimetric SAR image classification -- Wishart distance‐based joint collaborative representation; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0056
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Inspired by collaborative representation classifier (CRC), a Wishart distance‐based joint CRC (W‐JCRC) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. Since that neighbouring pixels usually belong to the same category with high probability, they can be simultaneously represented via a joint representation model of linear combinations of labelled samples. The joint collaborative representation of neighbouring pixels can overcome the influence of speckle noise at the same time. Considering the statistical property of PolSAR data, a weighted regularisation term with revised Wishart distance is designed to contain the correlations between unlabelled and labelled samples. The coefficients of representation are estimated by an l 2 ‐norm minimisation derived closed‐form solution. In the experiments, three real PolSAR images are applied to evaluate the performance, and the experimental results demonstrate that the proposed method is able to improve classification accuracies compared with other state‐of‐the‐art methods.