Volume XXXIX-B4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B4, 363-368, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B4-363-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B4, 363-368, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B4-363-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

AN ITERATIVE TERRAIN RECOVERY APPROACH TO AUTOMATED DTM GENERATION FROM AIRBORNE LIDAR POINT CLOUDS

H. Chen1, M. Cheng2, J. Li2,1, and Y. Liu3 H. Chen et al.
  • 1GeoSTARS Lab, Department of Geography and Environmental Management, University of Waterloo, 200 University Ave. West, Waterloo, ON, Canada N2L 3G1
  • 2GeoSTARS Group, School of Information Science and Engineering, Xiamen University, 422 Siming Road South, Xiamen, Fujian, China 361005
  • 3Hunan Provincial Transport Technical Information Center, Changsha, Hunan, China

Keywords: DTM, LiDAR, Terrain filtering, Multi-scale, Pyramid level

Abstract. This paper presents a hierarchical recovery method to generate DTMs from airborne LiDAR point clouds based one an idea of layering. The developed method first registers the last return points, and then layering them. The layering is done by dividing the points into different height layers and assigning layer numbers to each point. The layer numbers are comparing references in later identification process. Then a series of rasterized pyramid levels which consisted of lowest points are generated. Since the outliers have been removed after the layering, the cells in top level are considered to be terrain points and used as reference to identify cells in the following level. After the identification of the second level, an interpolation will occur in the cells which identified as offterrain. And the interpolated level will be used as reference in its following level and the same process is repeated at each level. Once this process of the bottom level finished, the proposed method adjusts the results based on the first return feedback and followed by the final interpolation. As a result, this produces the final DTM. The developed method is data driven, and does not assume a prior knowledge about the scene complexity. The proposed method was tested with the ISPRS WG III/3 LiDAR datasets covering different terrain types and filtering difficulties. The results show that the proposed method can perform well in flat terrain or gentle slope area.