具有细节保护的自适应邻域SAR图像分割

SAR Image Segmentation with Detail Preserving Based on Adaptive Neighborhoods

为了保护图像中边缘或其它细节信息,改善 MRF 的分割效果,提出自适应邻域方法.该方法利用 Bayes 推理实现将像素点周围的局部图像信息结合,此过程引入模糊隶属度作为像素相似度度量方法,提高置信度的可靠性和分割过程的自适应性,使得分割过程中的邻域选择可以不依赖于某些预知的先验知识.对于待选择的邻域系统,具有最高置信度且满足置信度阈值的邻域作为 MRF 类别标识分割过程适用的最小邻域.实验结果表明,与固定邻域 MRF 和隐 Markov 随机场相比,本文方法改善了图像分割效果,有效保护了图像中的细节信息.

An adaptive neighborhood approach is proposed. The Markov random fields （MRF） segmentation approach with adaptive neighborhood systems is utilized to preserve detail features and border areas and to improve the segmentation effect. Bayesian inference is applied to integrate the different information sources of local image around the pixels. To improve the reliability of the belief value and the adaptivity, fuzzy c-means （FCM） clustering is introduced in Bayesian network. Thus, the selection of the neighborhood in the region label process need not depend on the known priori knowledge by applying the FCM. The neighborhood with the highest belief value in the threshold scope is chosen to compute the MRF region label process. Experimental results demonstrate that the segmentation effect of the proposed algorithm is superior to that of the classical MRF and hidden Markov random field with detail structures well preserved.