ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume II-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 181–188, 2014
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 181–188, 2014

  07 Aug 2014

07 Aug 2014

Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features

M. Weinmann1, B. Jutzi1, and C. Mallet2 M. Weinmann et al.
  • 1Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 2IGN, MATIS lab., Université Paris Est, Saint-Mandé, France

Keywords: LIDAR, Point Cloud, Neighborhood Size Selection, Features, Feature Selection, Classification, Interpretation

Abstract. 3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results.