Land Cover Classification for Polsar Images Based on Mixture Models and MRF

Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Markov Random Field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this study, we first introduce two ways to construct the Wishart mixture model, and combine these models with MRF. Then, we use these models to implement pixel-based classification using a real PolSAR image. Finally, we analyze the experimental results and evaluate the robustness and applicability of the model mixture strategies.