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

doi:10.11947/j.AGCS.2018.20160607

Abstract

Abstract: 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.

Key words: SAR image change detection, fuzzy topology, adaptive distance, fuzzy clustering algorithm, FATCD

CLC Number:

P237

WANG Jianming, SHI Wenzhong, SHAO Pan. Change-detection Method for SAR Image Using Adaptive Distance and Fuzzy Topology Optimization-based Fuzzy Clustering[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(5): 611-619.

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