结合KL距离与图像域分块的SAR图像分割

提出一种结合KL(Kullback-Leibler)距离和图像域分块的SAR图像分割算法.首先,利用规则划分技术将图像域划分成若干规则子块,以子块为处理单元,假设子块内像素服从高斯分布,并构建特征场概率模型;其次,采用广义Potts模型定义刻画邻域子块相关性的标号场概率模型,根据贝叶斯定理,得到后验概率模型;再次,采用KL距离定义刻画同质区域间统计分布差异的异质性系数,并通过非约束吉布斯表达式构建概率分布函数,结合后验概率和吉布斯概率分布函数建立图像分割模型;然后,设计M-H(Metropolis-Hastings)采样方法,包括改变子块标号操作和分裂子块操作,模拟上述分割模型,从而获得最优分割结果;最后,通过对所提出算法和对比算法的SAR图像分割结果进行分析,充分验证了所提出算法的有效性和优越性.

Segmentation of SAR image based on Kullback-Leibler distance and regular tessellation

In this paper, a segmentation method for synthetic aperture radar(SAR) images based on Kullback-Leibler(KL) distance and regular tessellation is proposed. Firstly, the image domain is divided into several sub-blocks by a regular tessellation, and the divided blocks are considered as basic processing units during segmentation. It is assumed that all pixels in a sub-block follow Gaussian distribution, while to modeling feature field of a given image. Then a general Potts model is utilized to model relationship between neighbor sub-blocks in label field. According to Bayes theorem, the posterior probability model is obtained by combining pixels'' feature and sub-blocks'' labels. Thereafter, the heterogeneity coefficient between classes is characterized with KL distance, and the corresponding probability distribution function is constructed by a non-constrained Gibbs distribution. Combining the posterior probability model and the non-constrained Gibbs distribution, the image segmentation model is established. In order to simulate the segmentation model, a Metropolis-Hastings(M-H) sampling method is designed, including the operations of changing label and splitting sub-blocks. By analyzing the segmentation results of the proposed algorithm and the comparing algorithms, the validity and superiority of the proposed algorithm are fully verified.