Variational Learning of Mixture Wishart Model for PolSAR Image Classification

The phase difference, amplitude product, and amplitude ratio between two polarizations are important discriminators for terrain classification, which derives a significant statistical-distribution-based polarimetric synthetic aperture radar (PolSAR) image classification. Traditionally, statistical-distribution-based PoISAR image classification models pay attention to two aspects: searching for a suitable distribution to model certain PoISAR image and a satisfactory solution for the corresponding distribution model with samples in every terrain. Usually, the described distribution form is too complicated to build. Besides, inaccurate parameter estimation may lead to poor classification performance for PoISAR image. In order to refrain from this phenomenon, a variational thought is adopted for the statistical-distribution-based PoISAR classification method in this paper. First, a mixture Wishart model is built to model the PoISAR image to replace the complicated distribution for the PoISAR image. Second, a learning-based method is suggested instead of inaccurate point estimation of parameters to determine the distribution for every class in the mixture Wishart model. Finally, the proposed learning-based mixture Wishart model will be built as a variational form to realize a parametric model for PoISAR image classification. In the experiments, it will be proved that the class centers are easier to distinguish among different terrains learned from the proposed variational model. In addition, a classification performance on the PoISAR image is superior to the original point estimation Wishart model on both visual classification result and accuracy.