Abstract

Terrestrial laser scanning is of increasing importance for surveying and hazard assessments. Digital terrain models are generated using the resultant data to analyze surface processes. In order to determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. Filtering in mountainous terrain is more difficult than in other topography types. Here, existing automatic filtering solutions are not acceptable, because they are usually designed for airborne scan data. The present article describes a method specifically suitable for filtering terrestrial laser scanning data. This method is based on the direct line of sight between the scanner and the measured point and the assumption that no other surface point can be located in the area above this connection line. This assumption is only true for terrestrial laser data, but not for airborne data. We present a comparison of the wedge filtering to a modified inverse distance filtering method (IDWMO) filtered point cloud data. Both methods use manually filtered surfaces as reference. The comparison shows that the mean error and root–mean-square-error (RSME) between the results and the manually filtered surface of the two methods are similar. A significantly higher number of points of the terrain surface could be preserved, however, using the wedge-filtering approach. Therefore, we suggest that wedge-filtering should be integrated as a further parameter into already existing filtering processes, but is not suited as a standalone solution so far.

Highlights

  • Laser scanning provides point-sample elevation data, which enables the automated and fast generation of Digital Elevation Models (DEM) that can provide information on the morphological features of terrain, vegetation and buildings

  • We suggest that wedge-filtering should be integrated as a further parameter into already existing filtering processes, but is not suited as a standalone solution so far

  • Different approaches for filtering laser scanning data exist: the auto-regressive process [2], mathematical morphology [3], method of least squares, robust interpolation [4], convex-concave cover [5], and procedures that use, based on a triangular meshing (TIN) of the digital elevation model (DEM), the local terrain inclination as filter criterion [6,7], gridding methods in which a grid DEM is calculated trough including gradient based height values determined in a hierarchical data pyramid [8,9,10], method of multiscale curvature classification [11]

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Summary

Introduction

Laser scanning provides point-sample elevation data, which enables the automated and fast generation of Digital Elevation Models (DEM) that can provide information on the morphological features of terrain, vegetation and buildings. Different approaches for filtering laser scanning data exist: the auto-regressive process [2], mathematical morphology [3], method of least squares, robust interpolation [4], convex-concave cover [5], and procedures that use, based on a triangular meshing (TIN) of the DEM, the local terrain inclination as filter criterion [6,7], gridding methods in which a grid DEM is calculated trough including gradient based height values determined in a hierarchical data pyramid [8,9,10], method of multiscale curvature classification [11] These methods filter data obtained from airborne laser scanning. Prokop and Panholzer [14]

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