A novel polarimetric-texture-structure descriptor for high-resolution PolSAR image classification

A novel Polarimetric-Texture-Structure descriptor for high-resolution PolSAR image is presented in this paper. More precisely, a PolSAR image is represented by a tree of shapes, each of which is associated with several polarimetric and texture attributes. We first extract the texture properties and polarimetric characteristics from each shape, then use the shape co-occurrence patterns (SCOPs) to characterize the shape relationships, and finally use the resulting SCOPs distributions as features for PolSAR image classification. The proposed method not only has the strong ability to depict the texture and polarimetric properties, but also encodes the shape relationships on the tree. We compare the proposed method with the cluster based statistical feature (CSF) and the scattering mechanism based statistical feature (SMSF). Experimental results on high-resolution PolSAR sample dataset and a large scene for classification demonstrate the effectiveness of the proposed method.