The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 313–320, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-313-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 313–320, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-313-2021

  28 Jun 2021

28 Jun 2021

3D SURFACE RECONSTRUCTION FROM MULTI-DATE SATELLITE IMAGES

S. Bullinger, C. Bodensteiner, and M. Arens S. Bullinger et al.
  • Fraunhofer IOSB, Ettlingen, Germany

Keywords: Satellite Imagery, 3D Reconstruction, Photogrammetry, Surface Reconstruction, Texturing

Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.