Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 321-325, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-321-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 321-325, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-321-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS FROM HEIGHT FEATURES

M. Zheng1,2, M. Lemmens2, and P. van Oosterom2 M. Zheng et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
  • 2Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, the Netherlands

Keywords: Point clouds, Mobile Laser Scanning, Feature extraction, Classification, Urban area, Vertical objects

Abstract. The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features – two height components and one reflectance value, and achieved an overall accuracy of 73 %, which is really encouraging for further refining our approach.