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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 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 languageEnglish
Title of host publicationProceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages3248-3253
Number of pages6
Volume2006
DOIs
Publication statusPublished - 1 Dec 2006
Event2006 International Conference on Machine Learning and Cybernetics - Dalian, China
Duration: 13 Aug 200616 Aug 2006

Conference

Conference2006 International Conference on Machine Learning and Cybernetics
CountryChina
CityDalian
Period13/08/0616/08/06

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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