Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7762
Title: Road central contour extraction from high resolution satellite image using tensor voting framework
Authors: Zheng, S
Liu, J
Shi, WZ 
Zhu, GX
Keywords: High-resolution satellite image
Road central line extraction
Tensor voting framework
Issue Date: 2006
Source: Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, 2006, v. 2006, 4028627, p. 3248-3253 How to cite?
Abstract: In this paper, a unique road contour extraction approach from high resolution satellite image is proposed, in which the road contour was extracted in two steps. Firstly, support vector machines (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. The identified road group images are the discrete and irregularly distributed sampled points, and they are an uncompleted data set for the road. Secondly, the road contour was extracted from the road group images using the tensor voting framework, since the tensor voting technique is superior to the traditional methods in extracting the geometrical structure from the uncompleted data set. The experimental results on the high resolution satellite image demonstrate that the proposed approach worked well with images comprised by both rural and urban area features.
Description: 2006 International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006
URI: http://hdl.handle.net/10397/7762
ISBN: 1424400619
9781424400614
DOI: 10.1109/ICMLC.2006.258435
Appears in Collections:Conference Paper

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