Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

88 Citations (Scopus)

Abstract

Road centerline extraction from remotely sensed imagery can be used to update a Geographic Information System (GIS) database. The common road extraction from high-resolution imagery is based on spectral information only; it is difficult to separate road features from background completely, and a thinning algorithm always results in short spurs which reduce the smoothness of the road centerline. To overcome the aforementioned shortcomings of the common existing road centerline algorithms, this letter presents a new method to extract the road centerline from high-resolution imagery based on shape features and multivariate adaptive regression splines (MARS), in which potential road segments were obtained based on shape features and spectral feature, followed by MARS to extract road centerlines. Two experiments are performed to evaluate the accuracy of the proposed method.
Original languageEnglish
Article number6329404
Pages (from-to)583-587
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume10
Issue number3
DOIs
Publication statusPublished - 1 Jan 2013

Keywords

  • High-resolution imagery
  • multivariate adaptive regression splines (MARS)
  • road centerline extraction
  • shape feature

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Geotechnical Engineering and Engineering Geology

Cite this