Digital terrain models derived from digital surface model uniform regions in urban areas

Abstract

Digital terrain models (DTMs) are of significant interest for applications such as environment planning, flood risk assessment or building detection. A digital surface model (DSM) can be obtained efficiently in both time and cost from light detection and ranging (lidar) acquisition or from digital photogrammetry with aerial or satellite stereoscopic imagery. A DTM can be derived from a DSM if the distinction between ground and non-ground pixels can be automated. We propose in this article a new automatic DSM-to-DTM transform targeting urban areas. Our approach segments the DSM twice: first to get large uniform regions normally corresponding to the road network and attached town squares, and second to obtain smoother areas. Smoother DSM areas overlapping the large regions are selected to populate the DTM, which is then completed by a hierarchical interpolation procedure. As a refinement step, unused smoother regions lying under this DTM are added to create, after interpolation, the final DTM. This approach is positioned relative to the literature about segmentation-based lidar ground filtering. The procedure was developed for DSM rasters. Since the DTM extraction is intended to be applied to large images, special attention was devoted to optimize image processing tasks relative to memory usage and execution time. The proposed development was integrated in a building detection procedure and validated qualitatively in the context of a benchmark on urban object detection of the International Society for Photogrammetry and Remote Sensing (ISPRS). It was also applied to Brussels data for which lidar DTMs are available. The DTM comparison supports the correctness of our solution although difficulties may be encountered in off-terrain regions surrounded by higher regions and some interiors of city blocks. A final test on a rural and peri-urban scene opens positive perspectives for scenes more general than urban areas.