Method based on edge constraint and fast marching for road centerline extraction from very high-resolution remote sensing images

Lipeng Gao, Wen Zhong Shi, Zelang Miao, Zhiyong Lv

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements.
Original languageEnglish
Article number900
JournalRemote Sensing
Issue number6
Publication statusPublished - 1 Jun 2018


  • Edge constraint
  • Fast marching method
  • Road extraction
  • Semiautomatic
  • Very high-resolution image

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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