Polarimetric SAR image segmentation using texture partitioning and statistical analysis
This paper presents a new technique for partitioning polarimetric synthetic aperture radar (POL-SAR) imagery into regions of homogeneous polarimetric backscattering properties. The method consists of two cascaded steps: an initial texture segmentation of one image derived from the POL-SAR data, and a polarimetric statistical region merging process that refines and modifies the initial segmentation. In the first stage, a morphological region-based image partitioning technique, the watershed algorithm, plays the key role. While in the second stage, the region adjacency graph and the segment dissimilarity measures derived from the Wishart model and the K distribution are applied. The overall segmentation algorithm has been tested using real 4-look POL-SAR imagery and sample results are provided in the paper. The main innovation of this work is the utilization of the K distribution in the segmentation process.