DSM Generation from High Resolution Multi-View Stereo Satellite Imagery

Abstract

Along with improvements to spatial resolution, multiple-view stereo satellite imagery has become a valuable datasource for digital surface model generation. In 2016, a public multi-view stereo benchmark of commercial satellite imag- ery was released by the John Hopkins University Applied Physics Laboratory, USA. Motivated by this well-organized benchmark, we propose a pipeline to process multi-view satellite imagery into digital surface models. Input images are selected based on view angles and capture dates. We apply the relative bias-compensated model for orientation, and then generate the epipolar image pairs. The images are matched by the modified tube-based SemiGlobal Matching method (tSGM). Within the triangulation step, very dense point clouds are produced, and are fused by a median filter to generate the Digital Surface Model (DSM). A comparison with the reference data shows that the fused DSM generated by our pipeline is accurate and robust.