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      Classification of Polarimetric SAR Images Based on the Riemannian Manifold

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          Abstract

          Classification is one of the core components in the interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. A new PolSAR image classification approach employs the structural properties of the Riemannian manifold formed by PolSAR covariance matrices. In this paper, we first review the Riemannian manifold metrics generally used in PolSAR image analysis. Then, we describe a sparse coding method for the covariance matrices in the Riemannian manifold. For supervised classification, we propose a PolSAR image classification method that considers spatial information based on kernel space sparse coding. As for unsupervised PolSAR image classification, a method that takes advantage of Riemannian sparse induced similarity is proposed. Experimental results on EMISAR and AIRSAR data demonstrate the effectiveness of the proposed methods.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 October 2017
          : 6
          : 5
          : 433-441
          Affiliations
          [1 ] (School of Electronic Information, Wuhan University, Wuhan 430072, China)
          Article
          8bf38685500e4648a74a076c328a9a52
          10.12000/JR17031

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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