A semi-automatic method for road centerline extraction from VHR images

Zelang Miao, Bin Wang, Wen Zhong Shi, Hua Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

60 Citations (Scopus)

Abstract

This letter presents a semi-automatic approach to delineating road networks from very high resolution satellite images. The proposed method consists of three main steps. First, the geodesic method is used to extract the initial road segments that link the road seed points prescribed in advance by users. Next, a road probability map is produced based on these coarse road segments and a further direct thresholding operation separates the image into two classes of surfaces: the road and nonroad classes. Using the road class image, a kernel density estimation map is generated, upon which the geodesic method is used once again to link the foregoing road seed points. Experiments demonstrate that this proposed method can extract smooth correct road centerlines.
Original languageEnglish
Article number6784347
Pages (from-to)1856-1860
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume11
Issue number11
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • geodesic method
  • Kernel density estimation (KDE)
  • mean shift
  • road extraction
  • semi-automatic
  • very high resolution (VHR) satellite images

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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