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6 August 2002 New method of feature extraction in polarimetric SAR image classification
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Abstract
In this paper, a new method is proposed for supervised classification of ground cover types by using polarimetric synthetic aperture radar (SAR) data. The concept of similarity parameter between two scattering matrices is introduced and it is shown to be able to maintain some intrinsic properties of scattering mechanism. Four similarity parameters of each pixel in image are used for classification. The scattering matrix span of each pixel is also used to establish the feature space. The principal component analysis is adopted for extracting the feature transform vector and for making classification decision. The classification result of the new method is given with comparison to that of the maximum likelihood method, demonstrating the effectiveness of the proposed scheme.
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Junyi Xu, Jian Yang, and Ying-Ning Peng "New method of feature extraction in polarimetric SAR image classification", Proc. SPIE 4741, Battlespace Digitization and Network-Centric Warfare II, (6 August 2002); https://doi.org/10.1117/12.478729
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