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. Due to its simplicity, image differencing represents a popular approach for change detection. It is based on the idea to generate a difference image that represents the modulus of the spectral change vector associated to 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. In the present work, several simple thresholding methods are investigated and compared. The combination of the Expectation-Maximization algorithm with a thresholding method is also considered with the aim of achieving a better estimation of the optimal threshold value. For experimental purpose, a study area affected by a forest fire is considered. Two Landsat TM images of the area acquired before and after the event are utilized to reveal the burned zones and to assess and compare the above mentioned unsupervised change detection methods.