A comparison of clustering fully polarimetric SAR images using SEM algorithm and G0P mixture modelwith different initializations

This paper presents a comparison between two types of initializations for multilook polarimetric SAR image segmentation: a random partition and a sample quantile partition. These are the inputs of a stochastic expectation-maximization algorithm that uses a mixture of G0 P distributions to describe the data. The parameters are unknown, and estimated by the moments method. The G0 P law is able to describe different type of targets, like urban areas, vegetation and pasture. The experimental results on real PolSAR data are reported, showing that the use of G0 P model with quantile partition inicialization provide good segmentation results with few iterations.