Segmentation of multi-look fully polarimetric SAR images based on Wishart distribution and MRF

Abstract

Information contained in fully polarimetric SAR data is plentiful. How to exploit the information to improve accuracy is important in segmentation of fully polarimetric SAR images. Several frequently used feature vectors and methods are investigated, and a novel method is proposed for segmenting multi-look fully polarimetric SAR images in this paper, starting from the statistical characteristic and the interaction between adjacent pixels. In order to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, the Wishart distribution of the covariance matrix is integrated with the Markov random field, then the iterated conditional modes (ICM) is taken to implement the maximum a posteriori estimation of pixel labels. Although the ICM has good robustness and fast convergence, it is affected easily by initial conditions, so the Wishart-based ML is used to obtain the initial segmentation map, in order to exploit completely the statistical a priori knowledge in the initial segmentation step. Using multi-look fully polarimetric SAR images, acquired by the NASA/JPL AIRSAR sensor, the new approach is compared with several other commonly used ones, better segmentation performance and higher accuracy are observed.