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

Abstract

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.