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Indexed by:会议论文

Date of Publication:2016-07-10

Included Journals:EI、CPCI-S、Scopus

Volume:2016-November

Page Number:3066-3069

Key Words:Classification; polarimetric synthetic aperture radar (PolSAR); collaborative representation; spatial regularization

Abstract: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.