Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture FeaturesReport as inadecuate




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EURASIP Journal on Advances in Signal Processing

, 2010:960831

Advances in Multidimensional Synthetic Aperture Radar Signal Processing

Abstract

The classification of polarimetric SAR image based on Multiple-Component Scattering Model MCSM and Support Vector Machine SVM is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix GCM, SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area DK, Denmark. The preliminary result indicates that this method can classify most of the areas correctly.

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Author: Lamei Zhang - Bin Zou - Junping Zhang - Ye Zhang

Source: https://link.springer.com/







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