Hierarchical semantic model and scattering mechanism based PolSAR image classification

Abstract

The hybrid terrain type, such as the urban area, is generated by the backscattering of the similar ground objects aggregated together. It is a challenge to classify the hybrid terrain types as semantic homogeneous regions for unsupervised polarimetric SAR (PolSAR) image classification, since there are sharp bright-dark variations in intensity. In this paper, a hierarchical semantic model (HSM) is proposed for PolSAR images, which overcomes the above shortcoming by constructing the primal-level and middle-level semantics. The primal-level semantic is a polarimetric sketch map consisting of sketch segments, which is the sparse representation of a PolSAR image. The middle-level semantic is a region map which can extract hybrid terrain types as semantic homogeneous regions from the sketch map. Mapped by the region map, a complex PolSAR scene is partitioned into hybrid, linear and homogeneous subspaces. Then, according to the characteristics of the three subspaces, different methods are adopted, and furthermore polarimetric information is involved to improve the classification result. Experimental results on PolSAR data sets with different bands and sensors demonstrate that the proposed method is superior to the state-of-the-art methods in region homogeneity especially for hybrid terrain types.