The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | 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
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

  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.