Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 83-90, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-83-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 83-90, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-83-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  09 Jun 2016

09 Jun 2016

VOLUME BASED DTM GENERATION FROM VERY HIGH RESOLUTION PHOTOGRAMMETRIC DSMS

B. Piltz, S. Bayer, and A. M. Poznanska B. Piltz et al.
  • German Aerospace Center (DLR), Institute of Optical Sensor Systems, Berlin Adlershof, Germany

Keywords: digital surface model; digital terrain model; multi-directional; ground filtering

Abstract. In this paper we propose a new algorithm for digital terrain (DTM) model reconstruction from very high spatial resolution digital surface models (DSMs). It represents a combination of multi-directional filtering with a new metric which we call normalized volume above ground to create an above-ground mask containing buildings and elevated vegetation. This mask can be used to interpolate a ground-only DTM. The presented algorithm works fully automatically, requiring only the processing parameters minimum height and maximum width in metric units. Since slope and breaklines are not decisive criteria, low and smooth and even very extensive flat objects are recognized and masked. The algorithm was developed with the goal to generate the normalized DSM for automatic 3D building reconstruction and works reliably also in environments with distinct hillsides or terrace-shaped terrain where conventional methods would fail. A quantitative comparison with the ISPRS data sets Potsdam and Vaihingen show that 98-99% of all building data points are identified and can be removed, while enough ground data points (~66%) are kept to be able to reconstruct the ground surface. Additionally, we discuss the concept of size dependent height thresholds and present an efficient scheme for pyramidal processing of data sets reducing time complexity to linear to the number of pixels, O(WH).