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Context-based urban terrain reconstruction from UAV-videos for geoinformation applications

: Bulatov, D.; Solbrig, P.; Gross, H.; Wernerus, P.; Repasi, E.; Heipke, C.

Fulltext urn:nbn:de:0011-n-1865146 (7.3 MByte PDF)
MD5 Fingerprint: 2a90bbef92ec4dd91908938777743457
Created on: 6.12.2011

Eisenbeiss, H. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
International Conference on Unmanned Aerial Vehicle in Geomatics, UAV-g 2011. Proceedings : September 14.-16. 2011, Zürich, Switzerland
Zurich, 2011 (International archives of photogrammetry, remote sensing and spatial information sciences XXXVIII-1/C22)
ISSN: 1682-1750
6 pp.
International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g) <2011, Zurich>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
UAV; photogrammetry; sensor orientation; urban terrain reconstruction

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 - 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 quasiintrasensorial 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.