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Context-based urban terrain reconstruction from images and videos

: Bulatov, Dimitri; Rottensteiner, Franz; Schulz, Karsten

Fulltext urn:nbn:de:0011-n-2142964 (938 KByte PDF)
MD5 Fingerprint: 63285e75bf29a26ec1d60abb57d9df9a
Created on: 26.9.2012

Shortis, M. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
XXII ISPRS Congress of the International Society for Photogrammetry and Remote Sensing 2012. Technical Commission III : 25. August - 1. September 2012 , Melbourne, Australien
Melbourne, 2012 (ISPRS Annals Vol.1-3, 2012)
International Society for Photogrammetry and Remote Sensing (ISPRS Congress) <22, 2012, Melbourne>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
building reconstruction; depth map; digital surface model; free geographic data; near infrared; vegetation

Detection of buildings and vegetation, and even more reconstruction of urban terrain from sequences of aerial images and videos is known to be a challenging task. It has been established that those methods that have as input a high-quality Digital Surface Model (DSM), are more straight-forward and produce more robust and reliable results than those image-based methods that require matching line segments or even whole regions. This motivated us to develop a new dense matching technique for DSM generation that is capable of simultaneous integration of multiple images in the reconstruction process. The DSMs generated by this new multi-image matching technique can be used for urban object extraction. In the first contribution of this paper, two examples of external sources of information added to the reconstruction pipeline will be shown. The GIS layers are used for recognition of streets and suppressing false alarms in the depth maps that were caused by moving vehicles while the near infrared channel is applied for separating vegetation from buildings. Three examples of data sets including both UAV-borne video sequences with a relatively high number of frames and high-resolution (10cm ground sample distance) data sets consisting of (few spatial-temporarily diverse) images from large-format aerial frame cameras, will be presented. By an extensive quantitative evaluation of the Vaihingen block from the ISPRS benchmark on urban object detection, it will become clear that our procedure allows a straight-forward, efficient, and reliable instantiation of 3D city models.