Dense, reliable, and depth discontinuity preserving DEM computation from H.R.V. urban stereopairs

In this paper, we develop a simple Digital Elevation Models processing scheme that focuses on the improvement of urban high resolution DEM basic properties such as density, reliability, accuracy, capacity to render 3-D landscape shapes and breaks to improve 3D-building models production but also urban orthophoto production. Our basic single scale matching algorithm is based on a cross-correlation template matching to provide the denser depth maps as possible. In this algorithm, template windows are not rectangular, they are landscape adaptive. Contour grey-level image features are used to define the matching window shape thus preserving clean, sharp and well located depth and slope discontinuities. A sub-pixel disparity estimation is also used to enhance the matching accuracy and thus provides a smoother 3-D scene surface. An internal validation of the disparity measurements based on the study of symmetrical correlation coherence enhances the reliability of the process but therefore leads to sparser maps. To obtain denser maps and to accelerate the matching process especially on very high resolution images where the disparity search intervals for the points to be matched can be very wide an thus lead to a combinatorial explosion, our single scale matching process is integrated in a multi-resolution matching strategy. We show that our processing scheme stands very good results on a set of complex and various urban scenes images of different resolutions and different sensors.