DSM Generation from Multi-View High-Resolution Satellite Images Based on the Photometric Mesh Refinement Method

Abstract

Automatic reconstruction of DSMs from satellite images is a hot issue in the field of photogrammetry. Nowadays, most state-of-the-art pipelines produce 2.5D products. In order to solve some shortcomings of traditional algorithms and expand the means of updating digital surface models, a DSM generation method based on variational mesh refinement of satellite stereo image pairs to recover 3D surfaces from coarse input is proposed. Specifically, the initial coarse mesh is constructed first and the geometric features of the generated 3D mesh model are then optimized by using the information of the original images, while the 3D mesh subdivision is constrained by combining the image’s texture information and projection information, with subdivision optimization of the mesh model finally achieved. The results of this method are compared qualitatively and quantitatively with those of the commercial software PCI and the SGM method. The experimental results show that the generated 3D digital surface has clearer edge contours, more refined planar textures, and sufficient model accuracy to match well with the actual conditions of the ground surface, proving the effectiveness of the method. The method is advantageous for conducting research on true 3D products in complex urban areas and can generate complete DSM products with the input of rough meshes, thus indicating it has some development prospects.