The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXVIII-1/C22
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-1/C22, 75–80, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-75-2011
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-1/C22, 75–80, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-75-2011

  06 Sep 2012

06 Sep 2012

CONTEXT-BASED URBAN TERRAIN RECONSTRUCTION FROM UAV-VIDEOS FOR GEOINFORMATION APPLICATIONS

D. Bulatov1, P. Solbrig1, H. Gross1, P. Wernerus1, E. Repasi1, and C. Heipke2 D. Bulatov et al.
  • 1Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Germany
  • 2Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany

Keywords: UAVs, Photogrammetry, Sensor Orientation, Urban Terrain Reconstruction

Abstract. Urban terrain reconstruction has many applications in areas of civil engineering, urban planning, surveillance and defense research. Therefore the needs of covering ad-hoc demand and performing a close-range urban terrain reconstruction with miniaturized and relatively inexpensive sensor platforms are constantly growing. Using (miniaturized) unmanned aerial vehicles, (M)UAVs, represents one of the most attractive alternatives to conventional large-scale aerial imagery. We cover in this paper a four-step procedure of obtaining georeferenced 3D urban models from video sequences. The four steps of the procedure – orientation, dense reconstruction, urban terrain modeling and geo-referencing – are robust, straight-forward, and nearly fully-automatic. The two last steps – namely, urban terrain modeling from almost-nadir videos and co-registration of models 6ndash; represent the main contribution of this work and will therefore be covered with more detail. The essential substeps of the third step include digital terrain model (DTM) extraction, segregation of buildings from vegetation, as well as instantiation of building and tree models. The last step is subdivided into quasi- intrasensorial registration of Euclidean reconstructions and intersensorial registration with a geo-referenced orthophoto. Finally, we present reconstruction results from a real data-set and outline ideas for future work.