Polarimetric SAR image classification using binary coding-based polarimetric-morphological features

Polarimetric synthetic aperture radar (POLSAR) systems provide high resolution images containing polarimetric information. So, they have high capability in land cover classification. In this work, a binary coding-based polarimetric-morphological (BCPM) feature extraction is proposed for POLSAR image classification. At first, a set of polarimetric features is proposed. Then, a new morphological framework is introduced for contextual feature extraction from the POLSAR cube. The coherence matrix is composed from diagonal and non-diagonal elements with different information. These elements are analysed separately in the proposed method. Moreover, the amplitude and phase components of the non-diagonal elements are individually analysed using morphological filters by reconstruction. Finally, a binary coding-based polarimetric-spatial feature reduction, which uses the first order statistics, is proposed for feature transformation. The experiments on three real POLSAR images and a synthetic dataset show the superior performance of BCPM compared to several classification methods.