Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 75-82, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 75-82, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

NEW DTM EXTRACTION APPROACH FROM AIRBORNE IMAGES DERIVED DSM

Y. A. Mousa1,2, P. Helmholz1, and D. Belton1 Y. A. Mousa et al.
  • 1Department of Spatial Sciences, Curtin University, Perth, WA, Australia
  • 2Al-Muthanna University, College of Engineering, Civil Engineering department, Al-Muthanna, Iraq

Keywords: Digital Surface Model (DSM), Digital Terrain Model (DTM), DTM extraction, normalised DSM (nDSM), airborne images, LiDAR

Abstract. In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm Multi-directional and Slope Dependent (MSD) by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III/4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.