The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 343–349, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-343-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 343–349, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-343-2018

  19 Sep 2018

19 Sep 2018

ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

M. Lemmens M. Lemmens
  • GIS 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, knowledge-based system, feature extraction, classification

Abstract. A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79 % for the Paris-rue-Madame point cloud bench mark data set.