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.
Highlights
Polarimetric SAR (PolSAR) image classification is arguably one of the most important applications in remote sensing
Multiple-Component Scattering Model (MCSM) is a general polarimetric target decomposition method, which can be applied to the symmetry and asymmetry reflection conditions [8]
MCSM is a general polarimetric target decomposition method, which can be applied to the symmetry and asymmetry reflection conditions
Summary
Polarimetric SAR (PolSAR) image classification is arguably one of the most important applications in remote sensing. Multiple-Component Scattering Model (MCSM) is a general polarimetric target decomposition method, which can be applied to the symmetry and asymmetry reflection conditions [8]. Some classification methods, such as statistic classifiers and Neural Network (NN) classifier [9], have been used in the classification.
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