A Variational Level-Set Method for Unsupervised Change Detection in Remote Sensing Images

In this paper, we propose a variational level-set method for unsupervised change-detection in remote sensing images. The discrimination between changed and unchanged classes in the difference image is achieved by defining an energy functional known as the piecewise constant approximation Mumford-Shah segmentation model. The minimization of this energy functional is realized according to an attractive level-set method seeking to find an optimal contour which splits the image into two mutually exclusive regions associated with changed and unchanged classes, respectively. In order to increase the robustness against the initialization issue, we adopt a multiresolution level-set approach by analyzing the difference image at different resolution levels. The experimental results obtained on two multitemporal remote sensing images acquired by low as well as very high spatial remote sensing sensors confirm the promising capabilities of the proposed approach.