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论文类型：会议论文

发表时间：2016-07-10

收录刊物：EI、CPCI-S、Scopus

卷号：2016-November

页面范围：3066-3069

关键字：Classification; polarimetric synthetic aperture radar (PolSAR); collaborative representation; spatial regularization

摘要：Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in PolSAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously represented through linear combinations of training samples. Therefore, a collaborative representation-based classifier with spatially joint regularization is adopted for classification. Experimental results demonstrate that the joint collaborative representation model performs better than other state-of-the-art methods, such as support vector machine and simultaneous sparse representation.