By Topic

Learning-based building outline detection from multiple aerial images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yanlin Guo ; Sarnoff Corp., Princeton, NJ, USA ; Sawhney, H.S. ; Kumar, Rakesh ; Hsu, S.

This paper presents a method for detecting building outlines using multiple aerial images. Since data-driven techniques may not be able to account for variability of building geometry and appearances, a key insight explored in this paper is a combination of model-based data driven front end with data driven learning in the back end for increased detection accuracy. The three main components of the detection algorithm are: (i) initialization. Image intensity and depth information are integrally used to efficiently detect buildings, and a robust rectilinear path finding algorithm is adopted to obtain good initial outlines. The initialization process involves the following steps: detecting location of buildings, determining the dominant orientations and knot points in the building outline and using these to fit the initial outline; (ii) learning. A compact set of building features are defined and learned from the well-delineated buildings, and a tree-based classifier is applied to the whole region to detect any missing buildings and obtain their rough outlines; and (iii) verification and refinement. Learned features are used to remove falsely detected buildings, and all outlines are refined by the deformation of rectilinear templates. The experiments, with improved detection rate and precise outlines, demonstrate the applicability of our algorithm.

Published in:

Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on  (Volume:2 )

Date of Conference: