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Proceedings Paper

Road extraction from remote sensing image using support vector machine
Author(s): Tiancan Mei; Feipeng Li; Qianqing Qin; Deren Li
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Paper Abstract

In this paper, we propose a technique that combined template matching and support vector machine for road identification from high-resolution aerial image. It is a model-driven approach that combines both the local and global criteria about the radiometry and geometry of linear structures interested. In this approach, the road center point is extracted by utilizing the general road model. Then the road center point is used as initial point for the template matching through which the road segment is obtained. The road characteristic is learned through the support vector machine that is based on the statistical learning theory. The support vector machine is a powerful learning method thatit can get high classification accuracy without too much training sample. These properties can be applied for extracting the road characteristics from few road samples. The support vector machine is used to extract the true road segment and remove the false road segment. The proposed approach has been experimented on high-resolution aerial image and its performance is satisfied.

Paper Details

Date Published: 25 September 2003
PDF: 6 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538941
Show Author Affiliations
Tiancan Mei, Wuhan Univ. (China)
Feipeng Li, Wuhan Univ. (China)
Qianqing Qin, Wuhan Univ. (China)
Deren Li, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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