ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 223-230, 2018
https://doi.org/10.5194/isprs-annals-IV-2-223-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
28 May 2018
HIGH QUALITY FACADE SEGMENTATION BASED ON STRUCTURED RANDOM FOREST, REGION PROPOSAL NETWORK AND RECTANGULAR FITTING
K. Rahmani and H. Mayer Bundeswehr University Munich, Institute for Applied Computer Science, Neubiberg, Germany
Keywords: Facade Segmentation, Model Fitting, CNN, Object Detection Abstract. In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF), Region Proposal Network (RPN) based on a Convolutional Neural Network (CNN) as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF.We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.
Conference paper (PDF, 4045 KB)

Citation: Rahmani, K. and Mayer, H.: HIGH QUALITY FACADE SEGMENTATION BASED ON STRUCTURED RANDOM FOREST, REGION PROPOSAL NETWORK AND RECTANGULAR FITTING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 223-230, https://doi.org/10.5194/isprs-annals-IV-2-223-2018, 2018.

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