  For polarimetric SAR (PolSAR) image classification, it is a challenge to
classify the aggregated terrain types, such as the urban area, into semantic
homogenous regions due to sharp bright-dark variations in intensity. The
aggregated terrain type is formulated by the similar ground objects aggregated
together. In this paper, a polarimetric hierarchical semantic model (PHSM) is
firstly proposed to overcome this disadvantage based on the constructions of a
primal-level and a middle-level semantic. The primal-level semantic is a
polarimetric sketch map which consists of sketch segments as the sparse
representation of a PolSAR image. The middle-level semantic is a region map
which can extract semantic homogenous regions from the sketch map by exploiting
the topological structure of sketch segments. Mapping the region map to the
PolSAR image, a complex PolSAR scene is partitioned into aggregated, structural
and homogenous pixel-level subspaces with the characteristics of relatively
coherent terrain types in each subspace. Then, according to the characteristics
of three subspaces above, three specific methods are adopted, and furthermore
polarimetric information is exploited to improve the segmentation 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 and edge preservation for terrain classification.
