Journal
REMOTE SENSING LETTERS
Volume 5, Issue 4, Pages 396-403Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/2150704X.2014.912766
Keywords
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Funding
- Ministry of Science and Technology of China [2012BAJ15B04]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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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.
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