Browsing by Subject "Conditional random field"

Browsing by Subject "Conditional random field"

Sort by: Order: Results:

  • Paul, A.; Yang, C.; Breitkopf, U.; Liu, Y.; Wang, Z.; Rottensteiner, F.; Wallner, M.; Verworn, A.; Heipke, C. (London : International Society for Photogrammetry and Remote Sensing, 2018)
    In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order ...
  • Hoberg, Thorsten; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2012)
    The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification ...
  • Niemeyer, Joachim; Rottensteiner, Franz; Sörgel, Uwe; Heipke, Christian (Hannover : International Society for Photogrammetry and Remote Sensing, 2015)
    In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: ...
  • Menze, Moritz; Heipke, Christian; Geiger, Andreas (Heidelberg : Springer Verlag, 2015)
    We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing ...
  • Niemeyer, Joachim; Rottensteiner, Franz; Sörgel, Uwe; Heipke, Christian (Göttingen : Copernicus GmbH, 2016)
    We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point ...
  • Albert, Lena; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2014)
    Geospatial land use databases contain important information with high benefit for several users, especially when they provide a detailed description on parcel level. Due to many changes connected with a high effort of the ...
  • Zhang, Z.; Yang, M.Y.; Zhoua, M. (Hannover : International Society for Photogrammetry and Remote Sensing, 2013)
    Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote ...