• DocumentCode
    244182
  • Title

    Extraction of Bridges from High Resolution Remote Sensing Image Based on Topology Modeling

  • Author

    Haitao Lu ; Yong Deng ; Zhijian Huang ; Jinfang Zhang

  • Author_Institution
    Sci. & Technol. on Integrated Inf. Syst. Lab., Inst. of Software, Beijing, China
  • fYear
    2014
  • fDate
    11-14 March 2014
  • Firstpage
    494
  • Lastpage
    499
  • Abstract
    Bridges are the hubs of transportation, so it is important to identify and locate bridges for satellite image interpretation. This paper proposes a new method of bridge extraction from high resolution remote sensing images. Firstly, river regions are segmented and road lineaments are extracted. Then, bridge region is represented as intersection of river with roads or roads with roads by using the recognition model proposed in this paper. Finally, a rule-based procedure is applied to verify candidate regions. The experiment results show that, not only bridges over river but also the overpasses can be extracted effectively. The method mainly uses structural information of lineaments on roads and the topological relations in bridge regions, and does not rely on accurate results of river segmentation, so it is robust for complex scenes.
  • Keywords
    bridges (structures); feature extraction; geophysical image processing; image resolution; image segmentation; knowledge based systems; remote sensing; bridge extraction; high resolution remote sensing image; linear feature extraction; recognition model; river segmentation; rule-based procedure; satellite image interpretation; topology modeling; Bridges; Data mining; Feature extraction; Image resolution; Image segmentation; Rivers; Roads; bridge extraction; high resolution remote sensing image; linear feature; topological information;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Engineering (IC2E), 2014 IEEE International Conference on
  • Conference_Location
    Boston, MA
  • Type

    conf

  • DOI
    10.1109/IC2E.2014.79
  • Filename
    6903517