Unsupervised Change Detection in Multispectral Remotely Sensed Imagery With Level Set Methods

In this paper, the unsupervised change-detection problem in remote sensing images is formulated as a segmentation issue where the discrimination between changed and unchanged classes in the difference image is achieved by defining a proper energy functional. The minimization of this functional is carried out by means of a level set method which iteratively seeks to find a global optimal contour splitting the image into two mutually exclusive regions associated with changed and unchanged classes, respectively. In order to increase the robustness of the method to noise and to the choice of the initial contour, a multiresolution implementation, which performs an analysis of the difference image at different resolution levels, is proposed. The experimental results obtained on three different multitemporal remote sensing images acquired by low- as well as high-spatial-resolution optical remote sensing sensors suggest a clear superiority of the proposed approach compared with state-of-the-art change-detection methods.