Volume XL-1/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 293-297, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W4-293-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 293-297, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W4-293-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Aug 2015

26 Aug 2015

MODEL-BASED BUILDING DETECTION FROM LOW-COST OPTICAL SENSORS ONBOARD UNMANNED AERIAL VEHICLES

K. Karantzalos1, P. Koutsourakis1, I. Kalisperakis2, and L. Grammatikopoulos3 K. Karantzalos et al.
  • 1Remote Sensing Lab., National Technical University of Athens, Athens, Greece
  • 2up2metric PC, Athens, Greece
  • 3Laboratory of Photogrammetry, Technological Educational Institute of Athens, Athens, Greece

Keywords: Extraction, reconstruction, unsupervised, segmentation, bundle adjustment, dense matching, fusion, DSM, DTM

Abstract. The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.