Volume I-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 263-268, 2012
https://doi.org/10.5194/isprsannals-I-3-263-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 263-268, 2012
https://doi.org/10.5194/isprsannals-I-3-263-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  20 Jul 2012

20 Jul 2012

CONDITIONAL RANDOM FIELDS FOR LIDAR POINT CLOUD CLASSIFICATION IN COMPLEX URBAN AREAS

J. Niemeyer, F. Rottensteiner, and U. Soergel J. Niemeyer et al.
  • Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, D-30167 Hannover, Germany

Keywords: Classification, Urban, LiDAR, Point Cloud, Conditional Random Fields

Abstract. In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building, low vegetation, tree, natural ground, and asphalt ground. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely building and tree.