A Novel SAR Image Segmentation Method Based on Markov Random Field
一种基于马尔可夫随机场的SAR图像分割新方法

A novel Markow Random Field(MRF)-based segmentation method for SAR images is proposed. The method involves the intensity differences and the distances between pixels based on the traditional MRF potential function. It utilizes more spacial information of SAR image in the potential function model. The segmentation issue is transformed to the Maximum A Posteriori (MAP) by Beyes theorem. Finally, the Iterative Conditional Model (ICM) is employed to find out the solution of MAP problem. In the experiments, the method is compared with the traditional MRF segmentation method using ICM and simulate annealing, the results showed that this method is better than the traditional MRF one both in noise filtering and miss-classification ratio.