A Hierarchical Gamma Mixture Model Toward Hidden Markov Random Field for High-Resolution SAR Image Segmentation

Accurately modeling the distributions of spectral intensities is an effective way to obtain accurate segmentation results. Existing mixture models mostly fail to establish accurate statistical models of high-resolution SAR images due to the complex statistical features of their spectral intensities. We propose a hierarchical gamma mixture model (HGaMM)-based high-resolution SAR images segmentation algorithm. In the proposed algorithm, a statistical model for SAR images is built using HGaMM, which can model heavy-tailed, asymmetric, multimodal, or flat distributions with its hierarchical structure. The algorithm allows for the effective utilization of spectral intensity information required for image segmentation. The HGaMM consists of several components that serve as the first layer, and several elements under each component serve as the second layer. The component weight is constructed using posterior probabilities of local pixels aimed at reducing noise effects. The segmentation model can then be established by the Bayesian theorem. A new expectation maximization/adding or deleting Markov chain Monte Carlo (EM/ADMCMC) is incorporated to implement parameter estimation and determine the optimum number of components. Several experiments were implemented on simulated and real high-resolution SAR images to evaluate the applicability and efficiency of the proposed approach. The experimental results show that the proposed HGaMM algorithm outperforms traditional algorithms and can obtain accurate results.