Land Cover Classification with Cpolinsar Image via M-Delta Decomposition and Optimal Polarimetric Coherence Coefficient

Compact polarimetric interferometric synthetic aperture radar (CPolInSAR) has been widely used due to its low complexity and costs. But in the field of land cover classification, there are very few studies on CPolInSAR images. In this paper, a novel classification method is proposed to investigate the performance of CPolInSAR images for land cover classification. Specifically, the m - δ decomposition and the optimal polarimetric coherence coefficient are first employed to extract the features of CPolInSAR images, and then support vector machine (SVM) is utilized for classification. The experimental results show that 1) the optimal polarimetric coherence coefficient can be used to achieve higher accuracy especially in wood land and built-up areas; 2) CPolInSAR has a greater potential for land cover classification than PolSAR and compact PolSAR.