Road central contour extraction from high resolution satellite image using tensor voting framework

Sheng Zheng; Jian Liu; Wen Zhong Shi; Guang Xi Zhu

doi:10.1109/ICMLC.2006.258435

Road central contour extraction from high resolution satellite image using tensor voting framework

Sheng Zheng, Jian Liu, Wen Zhong Shi, Guang Xi Zhu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

10 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages	3248-3253
Number of pages	6
Volume	2006
DOIs	https://doi.org/10.1109/ICMLC.2006.258435
Publication status	Published - 1 Dec 2006
Event	2006 International Conference on Machine Learning and Cybernetics - Dalian, China Duration: 13 Aug 2006 → 16 Aug 2006

Conference

Conference	2006 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Dalian
Period	13/08/06 → 16/08/06

Keywords

High-resolution satellite image
Road central line extraction
Tensor voting framework

ASJC Scopus subject areas

General Engineering

More information

10.1109/ICMLC.2006.258435

Cite this

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title = "Road central contour extraction from high resolution satellite image using tensor voting framework",

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.",

keywords = "High-resolution satellite image, Road central line extraction, Tensor voting framework",

author = "Sheng Zheng and Jian Liu and Shi, {Wen Zhong} and Zhu, {Guang Xi}",

year = "2006",

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doi = "10.1109/ICMLC.2006.258435",

language = "English",

isbn = "1424400619",

volume = "2006",

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note = "2006 International Conference on Machine Learning and Cybernetics ; Conference date: 13-08-2006 Through 16-08-2006",

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Zheng, S, Liu, J, Shi, WZ & Zhu, GX 2006, Road central contour extraction from high resolution satellite image using tensor voting framework. in Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. vol. 2006, 4028627, pp. 3248-3253, 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13/08/06. https://doi.org/10.1109/ICMLC.2006.258435

Road central contour extraction from high resolution satellite image using tensor voting framework. / Zheng, Sheng; Liu, Jian; Shi, Wen Zhong et al.
Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. Vol. 2006 2006. p. 3248-3253 4028627.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Road central contour extraction from high resolution satellite image using tensor voting framework

AU - Zheng, Sheng

AU - Liu, Jian

AU - Shi, Wen Zhong

AU - Zhu, Guang Xi

PY - 2006/12/1

Y1 - 2006/12/1

N2 - 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.

AB - 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.

KW - High-resolution satellite image

KW - Road central line extraction

KW - Tensor voting framework

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SN - 9781424400614

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SP - 3248

EP - 3253

BT - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics

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Y2 - 13 August 2006 through 16 August 2006

ER -

Road central contour extraction from high resolution satellite image using tensor voting framework

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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