A novel dynamic threshold method for unsupervised change detection from remotely sensed images

Pengfei He, Wen Zhong Shi, Hua Zhang, Ming Hao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


In this letter, a dynamic threshold method is proposed for unsupervised change detection from remotely sensed images. First, change vector analysis technique is applied to generate the difference image. Then the statistical parameters of the difference image are estimated by Expectation Maximum algorithm assuming that the change and no-change pixel sets are modelled by Gaussian Mixture Model. As a result, a global initial threshold can be identified based on Bayesian decision theory. Next, a dynamic threshold operator is proposed by incorporating the membership value of each pixel generated by the Fuzzy c-means (FCM) algorithm and the global initial threshold. Lastly, the change map is obtained by segmenting the difference image utilizing the dynamic threshold proposed. Experimental results indicate that the proposed dynamic threshold method has significantly reduced the speckle noise comparing to the global threshold method. At the same time, weak change signals are detected and detail change information are preserved much better than the FCM does.
Original languageEnglish
Pages (from-to)396-403
Number of pages8
JournalRemote Sensing Letters
Issue number4
Publication statusPublished - 3 Apr 2014

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Electrical and Electronic Engineering

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