Robust Confidence Intervals for Digital Surface Models Using Satellite Photogrammetry

CNES has developed the CARS pipeline to massively produce Digital Surface Models (DSM) for remote sensing applications. DSM are computed from pairs of very high resolution satellite imagery using multi-view stereo methods. In this paper, we compute robust elevation confidence intervals with more than 90% accuracy alongside the high resolution DSM. We first estimate disparity confidence intervals using possibility distributions during the dense matching step, where most errors usually occur. Disparity confidence intervals are then processed with caution throughout every step of the pipeline to be transformed into elevation confidence intervals. Intervals accuracy is evaluated on both urban and glacier high resolution images.