Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15907
Title: A novel dynamic threshold method for unsupervised change detection from remotely sensed images
Authors: He, P
Shi, W 
Zhang, H
Hao, M
Issue Date: 2014
Publisher: Taylor and Francis Ltd.
Source: Remote sensing letters, 2014, v. 5, no. 4, p. 396-403 How to cite?
Journal: Remote Sensing Letters 
Abstract: 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.
URI: http://hdl.handle.net/10397/15907
ISSN: 2150-704X
DOI: 10.1080/2150704X.2014.912766
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