Road network extraction and intersection detection from aerial images by tracking road footprints

Jiuxiang Hu*, Anshuman Razdan, John C. Femiani, Ming Cui, Peter Wonka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

306 Scopus citations


In this paper, a new two-step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area-to-perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94%, correctness is above 81%, and quality is from 82% to 92%.

Original languageEnglish (US)
Pages (from-to)4144-4157
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number12
StatePublished - Dec 2007
Externally publishedYes


  • Bayes decision rule
  • Road extraction
  • Road footprint
  • Road tracking
  • Road tree pruning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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