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Proceedings Paper

Automatic building detection and 3D shape recovery from single monocular electro-optic imagery
Author(s): Daniel A. Lavigne; Parvaneh Saeedi; Andrew Dlugan; Norman Goldstein; Harold Zwick
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Paper Abstract

The extraction of 3D building geometric information from high-resolution electro-optical imagery is becoming a key element in numerous geospatial applications. Indeed, producing 3D urban models is a requirement for a variety of applications such as spatial analysis of urban design, military simulation, and site monitoring of a particular geographic location. However, almost all operational approaches developed over the years for 3D building reconstruction are semiautomated ones, where a skilled human operator is involved in the 3D geometry modeling of building instances, which results in a time-consuming process. Furthermore, such approaches usually require stereo image pairs, image sequences, or laser scanning of a specific geographic location to extract the 3D models from the imagery. Finally, with current techniques, the 3D geometric modeling phase may be characterized by the extraction of 3D building models with a low accuracy level. This paper describes the Automatic Building Detection (ABD) system and embedded algorithms currently under development. The ABD system provides a framework for the automatic detection of buildings and the recovery of 3D geometric models from single monocular electro-optic imagery. The system is designed in order to cope with multi-sensor imaging of arbitrary viewpoint variations, clutter, and occlusion. Preliminary results on monocular airborne and spaceborne images are provided. Accuracy assessment of detected buildings and extracted 3D building models from single airborne and spaceborne monocular imagery of real scenes are also addressed. Embedded algorithms are evaluated for their robustness to deal with relatively dense and complicated urban environments.

Paper Details

Date Published: 15 May 2007
PDF: 12 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 656716 (15 May 2007); doi: 10.1117/12.718326
Show Author Affiliations
Daniel A. Lavigne, Defence R&D Canada/Valcartier (Canada)
Parvaneh Saeedi, Simon Fraser Univ. (Canada)
Andrew Dlugan, MacDonald, Dettwiler & Associates (Canada)
Norman Goldstein, MacDonald, Dettwiler & Associates (Canada)
Harold Zwick, MacDonald, Dettwiler & Associates (Canada)

Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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