Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1225-1228, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1225-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1225-1228, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1225-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS OF URBAN SCENES EXPLOITING CYLINDRICAL NEIGHBOURHOODS

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 430079, China
  • 2GIS Technology Section, Department OTB, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands

Keywords: Point clouds, Mobile Laser Scanning, Classification, Feature extraction, Cylindrical Approach, 3D Mapping

Abstract. This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present within the cylindrical neighbourhood. Crucial in the approach is the selection of features from the points within the cylinder. An overall accuracy could be achieved, exploiting two bench mark data sets (Paris-rue-Madame and IQmulus & TerraMobilita) of 83 % and 87 % respectively.