Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 175-181, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-175-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 175-181, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-175-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

FACADE SEGMENTATION WITH A STRUCTURED RANDOM FOREST

K. Rahmani, H. Huang, and H. Mayer K. Rahmani et al.
  • Bundeswehr University Munich, Institute for Applied Computer Science, Visual Computing, Neubiberg, Germany

Keywords: Facade, Image interpretation, Structured learning, Random Forest

Abstract. In this paper we present a bottom-up approach for the semantic segmentation of building facades. Facades have a predefined topology, contain specific objects such as doors and windows and follow architectural rules. Our goal is to create homogeneous segments for facade objects. To this end, we have created a pixelwise labeling method using a Structured Random Forest. According to the evaluation of results for two datasets with the classifier we have achieved the above goal producing a nearly noise-free labeling image and perform on par or even slightly better than the classifier-only stages of state-of-the-art approaches. This is due to the encoding of the local topological structure of the facade objects in the Structured Random Forest. Additionally, we have employed an iterative optimization approach to select the best possible labeling.