Unsupervised change-detection methods for remote-sensing images

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, im- age 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 ap- plied. However, the selection of the best threshold value is not a trivial problem. We investigate and compare several simple thresholding meth- ods. 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 inves- tigation, 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 uti- lized to detect the burnt zones and to assess and compare the mentioned unsupervised change-detection methods.