Automatic urban area monitoring using digital surface models and shape features

Accurate monitoring of urban areas using remote sensing data requires reliable change detection techniques. Nevertheless, while most of the changes are optically visible and easily detectable by an expert user, automatic processes are quite difficult to develop. That is why, the interpretation of changes has remained up-to-now visual in most operational applications in remote sensing. This paper provides an automatic approach for 3D change detection based on the joint use of the height and spatial information. In fact, when dealing with urban areas, one possibility to cope with the automatic growth monitoring is the exploitation of the height information relative to the different man-made objects that exist in the scene. The subtraction of Digital Surface Models (DSMs), acquired at different epochs, should thus provide a valuable information about the 3D urban changes occurred in the studied area. However, when at least one of the DSMs presents some artifacts, a simple DSM subtraction could result also in the detection of virtual changes. To remove these virtual changes, we propose in this work to include, in addition to the height information, some shape features that could be of a great help in describing the geometry of the constructed or demolished man-made structures. After that, the Support Vector Machine (SVM) classifier is used to differentiate real from virtual changes. Evaluation of the proposed approach in terms of completeness, correctness, overall accuracy, etc has been performed proving its efficiency and relatively high accuracy.