Spectral-spatial classification and shape features for urban road centerline extraction

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

77 Citations (Scopus)


This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.
Original languageEnglish
Article number6594858
Pages (from-to)788-792
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number4
Publication statusPublished - 1 Apr 2014


  • High-resolution remotely sensed imagery
  • local Geary's C
  • main road extraction
  • path openings and closings
  • shape features
  • spectral-spatial classification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Geotechnical Engineering and Engineering Geology


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