Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 709–715, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-709-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 709–715, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-709-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

DENSE MULTI-VIEW IMAGE MATCHING FOR DSM GENERATION FROM SATELLITE IMAGES

A. Mahphood1, H. Arefi1, A. Hosseininaveh2, and A. A. Naeini3 A. Mahphood et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 2Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Iran
  • 3Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 8174673441, Iran

Keywords: DSM, Multi-View, Semi-Global Matching, Stereo Images, Dense Image Matching

Abstract. Digital Surface Model (DSM) can be generated from stereo pairs of satellite or aerial images. Among the most state-of-the-art matching algorithms, Semi-Global Matching (SGM) has widely been used for generating DSM from both satellite and aerial images. This paper presents an approach to improve the accuracy of DSM generated by SGM from multi-view satellite images using a novel technique including several filters. The filters are used for deleting mismatches between very tall buildings in urban areas and removing the sea regions. The technique, in contrast to the recent multi-view matching approaches, considers some of the points generated with only a pair of images in the final DSM. The approach is implemented on five sequential high resolution images acquired by the Worldview-2 satellite. The results are locally evaluated in shape and quantitative terms in comparison with commercial software to reveal the capability of the approach to generate a reliable and dense point cloud. Experiments show that the proposed method can achieve below half-pixel accuracy.