Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 17-22, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-17-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 17-22, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-17-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jul 2012

23 Jul 2012

A MULTI-SENSOR APPROACH TO SEMI-GLOBAL MATCHING

S. Gehrke1, M. Downey1, R. Uebbing1, J. Welter1, and W. LaRocque2 S. Gehrke et al.
  • 1North West Geomatics Ltd., Suite 212, 5438 - 11th Street NE, Calgary, Alberta, T2E 7E9, Canada
  • 2Intergraph Corp., 19 Interpro Road, Madison, AL 35758, USA

Keywords: Matching, Point Cloud, DEM/DTM, Surface, Multisensor, Aerial, High Resolution

Abstract. After we first presented the Semi-Global Matching (SGM) implementation for Leica ADS line-scanner data, the interest in applying this surface extraction to aerial frame imagery has increased. The reason is the combination of high-resolution geometry and multi-spectral information in the resulting point clouds. Such comprehensive point clouds or, more generic, information clouds (info clouds) allow for many different uses of the data, including applications that make currently use of LiDAR.

The DSM extraction tool for the ADS is based on SGM, which enables the derivation of disparity maps and eventually point clouds at the very image resolution. This approach was now extended to support both frame sensors and line-scanners in order to provide an integrated workflow for different sensor types. This paper describes how SGM is used in a sensor-agnostic system, based on few specific pre- and post-processing steps, within the DSM extraction tool we developed. Results from the ADS line-scanner as well as from DMC-II and RCD30 frame data are presented.