The Unsupervised Classification Method with KummerU Distribution of High Resolution PolSAR Images

The feature distribution of high-resolution polarimetric SAR data no longer conforms to the hypothesis of homogeneous region, which leads to the decline of unsupervised classification accuracy of polarimetric SAR image based on statistical distribution. A novel unsupervised classification algorithm for PolSAR image is proposed by embedding the widely applicable KummerU distribution into the Particle Swarm Optimization clustering algorithm. Firstly, the PolSAR data was classified based on the polarimetric statistical characteristics. Then combining the PolSAR statistical characteristics with PSO algorithm, a further solution to the clustering centers was found with the PSO-KummerU algorithm. In the part of the classification criteria, KummerU distance was used to replace the traditional Wishart distance to improve the classification result. Finally, 3 kinds of unsupervised classification methods (H/α-Wishart, PSO-Wishart, PSO-KummerU) were used for the comparison experiments. The experimental results show that the visual effect of PSO-KummerU clustering method based on KummerU distribution is significantly improved compared with the Wishart distance clustering method, and the overall classification accuracy is improved by more than 14%.