Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 513-518, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-513-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 513-518, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-513-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Aug 2012

01 Aug 2012

FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

M. Y. Yang1, W. Förstner2, and D. Chai3 M. Y. Yang et al.
  • 1Institute for Information Processing (TNT), Leibniz University Hannover
  • 2Department of Photogrammetry, Institute of Geodesy and Geoinformation, University of Bonn
  • 3Institute of Spatial Information Technique, Department of Earth Science, Zhejiang University

Keywords: Feature, evaluation, random decision forest, facade image

Abstract. The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.