利用RJMCMC算法的可变类SAR图像分割

自动确定地物类别数是SAR图像分割方法研究的重点和难点问题，为此，提出一种自动确定类别数的SAR图像分割算法。首先假定SAR图像中各像素强度服从同一独立的Gamma分布并以此建立图像模型；根据贝叶斯定理构建刻画图像分割的后验概率模型；设计RJMCMC（Reversible Jump Markov Chain Monte Carlo）算法模拟该后验概率模型，以确定图像类别数并同时完成区域分割。在提出的RJMCMC算法中，设计的移动操作类型包括:分裂或合并实类、改变参数矢量、改变标号及生成或删除空类。为了验证提出的可变类分割算法，分别对真实及模拟SAR图像进行可变类分割实验，定性及定量精度评价结果表明该算法的可行性及有效性。 

SAR Image Segmentation with Variable Classes Using RJMCMC Algorithm

In SAR image segmentation, automatically determining the number of classes is a critical and difficult problem. To this end, this paper presents a statistics based SAR image segmentation approach which can automatically determine the number of classes and segment the image simultaneously. First of all, a given SAR image is modeled on the assumption that intensities of its pixels satisfy identical and independent Gamma distributions. The Bayesian paradigm is followed to build image segmentation model. Then a RJMCMC (Reversible Jump Markov Chain Monte Carlo) scheme is utilized to govern the segmentation model, which determines the number of classes and segments the image. In the proposed RJMCMC algorithm, four move types are designed, including splitting or merging real classes, updating parameter vector, updating label field, birth or death of an empty class. In order to verify the proposed algorithm, testing is carried out with real and simulated SAR images, respectively, and the results show that the proposed algorithm works well and efficient.