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Proceedings Paper

Automated building extraction using dense elevation matrices
Author(s): A. A. Bendett; Urho A. Rauhala; James J. Pearson
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Paper Abstract

The identification and measurement of buildings in imagery is important to a number of applications including cartography, modeling and simulation, and weapon targeting. Extracting large numbers of buildings manually can be time- consuming and expensive, so the automation of the process is highly desirable. This paper describes and demonstrates such an automated process for extracting rectilinear buildings from stereo imagery. The first step is the generation of a dense elevation matrix registered to the imagery. In the examples shown, this was accomplished using global minimum residual matching (GMRM). GMRM automatically removes y- parallax from the stereo imagery and produces a dense matrix of x-parallax values which are proportional to the local elevation, and, of course, registered to the imagery. The second step is to form a joint probability distribution of the image gray levels and the corresponding height values from the elevation matrix. Based on the peaks of that distribution, the area of interest is segmented into feature and non-feature areas. The feature areas are further refined using length, width and height constraints to yield promising building hypotheses with their corresponding vertices. The gray shade image is used in the third step to verify the hypotheses and to determine precise edge locations corresponding to the approximate vertices and satisfying appropriate orthogonality constraints. Examples of successful application of this process to imagery are presented, and extensions involving the use of dense elevation matrices from other sources are possible.

Paper Details

Date Published: 26 February 1997
PDF: 10 pages
Proc. SPIE 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision, (26 February 1997); doi: 10.1117/12.267827
Show Author Affiliations
A. A. Bendett, GDE Systems, Inc. (United States)
Urho A. Rauhala, GDE Systems, Inc. (United States)
James J. Pearson, GDE Systems, Inc. (United States)


Published in SPIE Proceedings Vol. 2962:
25th AIPR Workshop: Emerging Applications of Computer Vision

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