自适应距离和模糊拓扑优化的模糊聚类SAR影像变化检测

针对模糊聚类算法的不足，结合差分影像的特点，提出一种基于自适应距离（adaptive distance）和模糊拓扑（fuzzy topology）理论的SAR影像变化检测技术框架（FATCD）。FATCD首先基于自适应距离公式提出一种自适应的样本到聚类中心的距离计算方法，优化了聚类过程中像元隶属度的计算公式，提高了模糊隶属度函数的准确程度；而后利用模糊拓扑理论改进传统去模糊化方式最大隶属度原则，从而增强了去模糊化过程。借助这两点，FATCD提高了模糊聚类变化检测的性能。两组真实SAR影像数据的试验结果表明本文方法可行、有效。

Change-detection Method for SAR Image Using Adaptive Distance and Fuzzy Topology Optimization-based Fuzzy Clustering

In this paper,a framework of change detection based on adaptive distance and fuzzy topology (FATCD)is proposed for synthetic aperture radar (SAR)imagery.FATCD integrates the characteristics of differenced image and can overcome the limitations of fuzzy C-means (FCM)type algorithms.The framework includes two key steps.First,a new adaptive method is employed to calculate the distances from samples to cluster centers using an adaptive distance function.As a result,the formula of pixel membership evaluation is modified,and the accuracy of the obtained fuzzy membership degree is improved. Then,fuzzy topology is integrated into the maximum membership rule to improve the traditional defuzzification method.In virtue of the above two points,FATCD can enhance the change detection performance of FCM-type algorithms.Experimental results on two different SAR images confirm the effectiveness of the proposed technique.