  This paper presents an automated pipeline for processing multi-view satellite
images to 3D digital surface models (DSM). The proposed pipeline performs
automated geo-referencing and generates high-quality densely matched point
clouds. In particular, a novel approach is developed that fuses multiple depth
maps derived by stereo matching to generate high-quality 3D maps. By learning
critical configurations of stereo pairs from sample LiDAR data, we rank the
image pairs based on the proximity of the results to the sample data. Multiple
depth maps derived from individual image pairs are fused with an adaptive 3D
median filter that considers the image spectral similarities. We demonstrate
that the proposed adaptive median filter generally delivers better results in
general as compared to normal median filter, and achieved an accuracy of
improvement of 0.36 meters RMSE in the best case. Results and analysis are
introduced in detail.
