Fast Road Network Extraction from Remotely Sensed Images

  • Vladimir A. Krylov
  • James D. B. Nelson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


This paper addresses the problem of fast, unsupervised road network extraction from remotely sensed images. We develop an approach that employs a fixed-grid, localized Radon transform to extract a redundant set of line segment candidates. The road network structure is then extracted by introducing interactions between neighbouring segments in addition to a data-fit term, based on the Bhattacharyya distance. The final configuration is obtained using simulated annealing via a Markov chain Monte Carlo iterative procedure. The experiments demonstrate a fast and accurate road network extraction on high resolution optical images of semi-urbanized zones, which is further supported by comparisons with several benchmark techniques.


Road network remote sensing localized Radon transform Markov chain Monte Carlo 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vladimir A. Krylov
    • 1
  • James D. B. Nelson
    • 1
  1. 1.Dept. of Statistical ScienceUniversity College LondonLondonUK

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