Facade Segmentation with a Structured 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.
- Publication:
-
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
- Pub Date:
- May 2017
- DOI:
- 10.5194/isprs-annals-IV-1-W1-175-2017
- Bibcode:
- 2017ISPAn41W1..175R