An unsupervised change detection problem can be viewed as a classification problem with only two classes corresponding to the change and no-change areas, respectively. Thanks to its simplicity, image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vector associated with each pixel in the study area. To separate the "change" and "no-change" classes in the difference image, a simple thresholding-based procedure can be applied. However, the selection of the best threshold value is not a trivial problem. We investigate and compare several simple thresholding methods. The combination of the expectation-maximization algorithm with a thresholding method is also performed for the purpose of achieving a better estimate of the optimal threshold value. As an experimental investigation, a study area damaged by a forest fire is considered. Two Land-sat TM images of the area acquired before and after the event are utilized to detect the burnt zones and to assess and compare the mentioned unsupervised change-detection methods.
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