Classification of polarimetric SAR image based on Support Vector Machine using Multiple-Component Scattering Model and texture features

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. © 2010 Lamei Zhang et al.