An integrated method for urban main-road centerline extraction from optical remotely sensed imagery

Wen Zhong Shi, Zelang Miao, Johan Debayle

Research output: Journal article publicationJournal articleAcademic researchpeer-review

178 Citations (Scopus)


Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction.
Original languageEnglish
Article number6567905
Pages (from-to)3359-3372
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
Publication statusPublished - 1 Jan 2014


  • General adaptive neighborhood (GAN)
  • local Geary's C
  • local linear kernel smoothing regression
  • optical remotely sensed images
  • shape feature
  • spectral-spatial classification
  • tensor voting
  • urban main-road centerline extraction

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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