Automatic generation of high resolution urban zone digital elevation models

Our paper presents an automatic generation of high resolution urban digital elevation models (DEMs) based on a highly redundant correlation process. We will discuss the difficulties of such a task by commenting on the state of the art, and we propose an approach in three main steps. In the first step, the image acquisition specification as image sequences leads to pairs with various base/height ratios in order to obtain good precision and few errors due to hidden parts. In the second step we use various stereovision methods and we merge the results, thus attributing to each pixel the most probable and precise elevation. In the third step we automatically extract terrain-DEM and building-DEM from computed DEM in order to specifically post-process each class. Finally, we combine these two DEMs to generate a final DEM which presents the best continuity for ground surface, and which respects sharp building discontinuities. The results obtained with an operational example (including image size, difficulty of the scene) demonstrate the feasibility of generating metric resolution urban data bases from automated digital stereo methods.