The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 127–134, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-127-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 127–134, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-127-2020

  12 Aug 2020

12 Aug 2020

GROUND TRUTH GENERATION AND DISPARITY ESTIMATION FOR OPTICAL SATELLITE IMAGERY

M. Cournet1, E. Sarrazin1, L. Dumas2, J. Michel1, J. Guinet2, D. Youssefi1, V. Defonte2, and Q. Fardet2 M. Cournet et al.
  • 1Centre National d’Etudes Spatiales (CNES), 18 avenue E. Belin, Toulouse cedex 9, France
  • 2CS, 5 rue Brindejonc des Moulinais, Toulouse Cedex 5, France

Keywords: Stereo Ground Truth, Disparity, Stereo-Matching, 3D, Pandora, Optical Satellite Imagery, CO3D

Abstract. Several 3D reconstruction pipelines are being developed around the world for satellite imagery. Most of them implement their own versions of Semi-Global Matching, as an option for the matching step. However, deep learning based solutions already outperform every SGM derived algorithms on Kitti and Middlebury stereo datasets. But these deep learning based solutions need huge quantities of ground truths for training. This implies that the generation of ground truth stereo datasets, from satellite imagery and lidar, seems to be of great interest for the scientific community. It will aim at reducing the potential transfer learning difficulties, that could arise from a training done on datasets such as Middlebury or Kitti. In this work, we present a new ground truth generation pipeline. It produces stereo-rectified images and ground truth disparity maps, from satellite imagery and lidar. We also assess the rectification and the disparity accuracies of these outputs. We finally train a deep learning network on our preliminary ground truth dataset.