Enhancing Urban Digital Elevation Models Using Automated Computer Vision Techniques

Abstract

In recent years Digital Elevation Models (DEM) gained much interest because of their high capability to give information about urban regions. DEM can be used for detailed urban monitoring, change and damage detection purposes. However, initially a DEM with very sharp details should be constructed. The DEM can be derived from very high resolution stereo satellite images, but for most of the cases just one stereo pair is available. Unfortunately after this process, regions which are occluded in one of the stereo images have no height value in the DEM data. This is a major problem especially in urban DEM, since many regions are occluded by buildings. However these occluded regions can be filled using interpolation techniques, which lead to lose sharpness in building edges. Besides due to low resolution of input stereo images, the generated DEM resolution can be too low to represent buildings. In order to increase details, herein we propose a special automated urban DEM enhancement technique. To do so, first we detect possible building locations using height information of the DEM. Then using corresponding panchromatic image, we detect building shapes with an automatic shape approximation approach. Using detected building shapes, we refine buildings in the DEM. Finally, for a better representation we locate constructed three-dimensional building models on Digital Terrain Model (DTM) of the corresponding region. We believe that the implemented enhancement will not only provide better three-dimensional urban region representation, but also will lead to more detailed change and damage investigation in future studies.