Volume 41 Issue 5
May  2016
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SUI Haigang, HUA Feng, FAN Yida, LIU Junyi. Road Damage Extraction from High-Resolution SAR Image Based on GIS Data and Bayes Network[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 578-583. DOI: 10.13203/j.whugis20140323
Citation: SUI Haigang, HUA Feng, FAN Yida, LIU Junyi. Road Damage Extraction from High-Resolution SAR Image Based on GIS Data and Bayes Network[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 578-583. DOI: 10.13203/j.whugis20140323
shu

Road Damage Extraction from High-Resolution SAR Image Based on GIS Data and Bayes Network

  • 1.

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;

  • 2.

    National Disaster Reduction Center of China, Ministry of Civil Affairs, Beijing 100124, China

Funds: The National Key Fundamental Research Plan of China, No. 2012CB719906; the National High Technology Research and Development Programme of China, No.2013AA122301; the National Natural Science Foundation of China, No. 41101414;Major Projects on High Resolution Earth Observation System.
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  • Received Date: November 30, 2014
  • Published Date: May 04, 2016
    Graphical Abstract
    • Abstract
  • Synthetic aperture radar (SAR) permits all-time, all-weather observation and strong penetration, and is widely used in disaster monitoring and evaluation, resource exploration, and military reconnaissance. However, speckle noise seriously affects the quality of SAR images, thus limiting the use of SAR image for quick access to information. In this paper, a new road damage extraction method for high-resolution SAR images based on GIS data and Bayes network is proposed. Guided by GIS data, suspected damaged roads are extracted using the fusion of level-set segmentation and an improved D1 line detection. A Bayes network is applied to further confirm real damaged roads based on multi-evidence and the observed values from suspected damage road, to eliminate the false-alarms from the SAR images. Experimental results indicate that our proposed method can extract road damage information quickly and accurately.
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    • References (9)
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