SAR Image Segmentation with Parallel MCMC Algorithm

The scheme of changing the pixels label variable values which are used to denoting the pixels class properties successively is usually used when using the MCMC (Markov Chain Monte Carlo, MCMC) algorithm, it usually leads to routine timeconsuming, slow speed of convergence. Therefore, a parallel MCMC scheme updating multiple pixels labels is proposed in this paper. In the framework of Bayesian inference, SAR (Synthetic Aperture Radar, SAR) image segmentation model is set up on the basis of Gaussian distribution and MRF (Markov Random Field, MRF) model. A parallel sampling scheme based on multithreading is designed; A independent pixel parallel sampling criterion is proposed so as to solve the label correlation problem between the neighborhood pixels labels in MRF label field; At the same time, the number of parallel threads is limited to ensure the sampling randomness. To verify the proposed parallel MCMC scheme, testing is carried out with real and simulated SAR images respectively by the serial algorithm and the proposed method. The results show that the proposed method behaves a significant reduction of running time without affecting the accuracy of segmentation.