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Proceedings Paper

Using gradients, alignment, and proximity to extract curves and connect roads in overhead images
Author(s): Barry Y. Chen; David W. Paglieroni
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Paper Abstract

A robust approach for automatically extracting roads from overhead images is developed in this paper. The first step involves extracting a very dense set of edge pixels using a technique based on the magnitude and direction of pixel gradients. In step two, the edges are separated into successive channels of edge orientation that each contain edge pixels whose gradient directions lie within a different angular range. A de-cluttered map of edge curve segments is extracted from each channel, and the results are merged into a single composite map of broken edge curves. The final step divides broken curves into segments that are nearly linear and classifies each segment as connected at both ends or disconnected. A measure of connectability between two disconnected line segments based on proximity and relative alignment is defined mathematically. Each disconnected segment is paired with the disconnected segment that it is most connectable to. Pairs of segments are merged if their separation and misalignment are below thresholds (manually specified at present) and the connectability of the pair is two-way optimal. Extended curve and road extraction examples are provided using commercial overhead images.

Paper Details

Date Published: 9 May 2006
PDF: 12 pages
Proc. SPIE 6203, Optics and Photonics in Global Homeland Security II, 62030P (9 May 2006); doi: 10.1117/12.668739
Show Author Affiliations
Barry Y. Chen, Lawrence Livermore National Lab. (United States)
David W. Paglieroni, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 6203:
Optics and Photonics in Global Homeland Security II
Theodore T. Saito; Daniel Lehrfeld, Editor(s)

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