Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 247-252, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/247/2014/
doi:10.5194/isprsarchives-XL-3-247-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
11 Aug 2014
Building Roof Component Extraction from Panchromatic Satellite Images Using a Clustering-Based Method
T. Partovi1, R. Bahmanyar1,2, T. Krauß1, and P. Reinartz1 1Institute of Remote Sensing Technology (IMF), German Aerospace Center (DLR), Wessling, Germany
2Munich Aerospace Faculty, Munich, Germany
Keywords: Pan-chromatic image, DSM, Roof Type Detection, Mask Refinement, Clustering Abstract. Developing fully automatic systems is still an active research topic in 3D building model reconstruction. While a general solution to the building reconstruction problem relies on collecting and grouping the modeling cues (e.g., lines, corners, planes) from Digital Surface Model (DSM) data, failure in finding the cues due to noise in the DSM and the object complexities is a big challenge. In this paper, we introduce a clustering-based method for cue discovery from Pan-chromatic satellite images which reduces the dependencies of the reconstruction techniques on DSM data. Experimental results show that the proposed method is not only able to effectively refine building masks by discriminating building boundaries from nearby clutter, but also is able to determine the roof types (e.g., pitched, flat). The latter, allows to establish a reconstruction method to reduces the search effort and the failure probability regions in finding a particular cue by leading the system to an appropriate area.
Conference paper (PDF, 3101 KB)


Citation: Partovi, T., Bahmanyar, R., Krauß, T., and Reinartz, P.: Building Roof Component Extraction from Panchromatic Satellite Images Using a Clustering-Based Method, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 247-252, doi:10.5194/isprsarchives-XL-3-247-2014, 2014.

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