结合规则划分和M-H算法的SAR图像分割

SAR Image Segmentation Combined Regular Tessellation and M-H Algorithm

提出了一种结合规则划分和M-H(Metropolis-Hastings)算法的SAR图像分割方法。首先,利用规则划分将图像域划分成子块,并假设每个子块内像素服从同一独立的Gamma分布;根据贝叶斯定理,构建基于子块的图像分割模型;然后,利用M-H算法模拟该分割模型,实现SAR图像分割及模型参数估计。在MH算法中,设计了改变参数矢量、改变标号场及分裂或合并子块三个移动操作。为了验证提出的分割方法,分别对真实及模拟SAR图像进行分割实验。定性及定量评价结果表明了本文方法的可行性及有效性。

This paper presents a SAR image segmentation method that combines regular tessellation and the Metropolis-Hastings（M-H）algorithm.First the image domain is partitioned into a group of rectangular sub-blocks by regular tessellation and then the image is modeled on the assumption that intensities of its pixels in each homogeneous region follow an identical and independent Gamma distribution.A region-based SAR image segmentation model is built using the Bayesian paradigm.Then,an M-H scheme is used to simulate the segmentation model,which can segment SAR image and estimate the model parameters.In the M-H algorithm,three move types are designated,including updating parameter vector,updating label field,and splitting or merging sub-block.The results obtained from both real and simulated SAR images show that the proposed algorithm works effectively and efficiently.