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

Jianming Wang, Wenzhong Shi, Pan Shao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)611-619
Number of pages9
JournalCehui Xuebao/Acta Geodaetica et Cartographica Sinica
Issue number5
Publication statusPublished - May 2018


  • Adaptive distance
  • Fuzzy clustering algorithm
  • Fuzzy topology
  • SAR image change detection

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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