Polarimetric SAR Image Classification using Multiple-Component Scattering Model and Support Vector Machine

摘要

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 singlebounce,uouble-bounce,volume,helix,and wire scattering components ar.extracted from full polarimetric SAR images.Combined with the scattering power and the texture feature,SVM is used for the polarimetric classification.We generate a validity test for the method using EMISAR L-band full polarized data of Foulum Area (DK),Denmark. The preliminary result indicates that this method can classify most of the areas correctly.