Acta Geodaetica et Cartographica Sinica ›› 2015, Vol. 44 ›› Issue (11): 1255-1262.doi: 10.11947/j.AGCS.2015.20140519

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SAR River Image Segmentation Based on Reciprocal Gray Entropy and Improved Chan-Vese Model

WU Shihua1, WU Yiquan1,2,3,4,5, ZHOU Jianjiang1, MENG Tianliang1   

  1. 1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 201116, China;
    2. Engineering Technology Research Center of Wuhan Intelligent Basin, Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China;
    3. Key Laboratory of the Yellow River Sediment of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Yellow River Water Resources Commission, Zhengzhou 450003, China;
    4. Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport, Nanjing Hydraulic Research Institute, Nanjing 210024, China;
    5. State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of Technology, Harbin 150090, ChinaAbstract
  • Received:2014-10-09 Revised:2015-03-23 Online:2015-11-20 Published:2015-11-25
  • Supported by:
    The National Natural Science Foundation of China(No. 60872065) Open Foundation of Changjiang River Scientific Research Institute(No. CKWV2013225/KY);Open Foundation of the Key Laboratory of the Yellow River Sediment of Ministry of Water Resources(No. 2014006);Open Foundation of the Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport;Open Foundation of the State Key Laboratory of Urban Water Resources and Environment(No. LYPK201304);The Fundamental Research Funds for the Central University Funding of Jiangsu Innovation Program for Graduate Education(No.SJLX15_0116);The Priority Academic Program Development of Jiangsu Higher Education Institution

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Abstract: To further improve the accuracy and speed of river segmentation on synthetic aperture radar(SAR) images, a segmentation method is proposed, which is based on improved Chan-Vese(CV) model combining with reciprocal gray entropy multi-threshold selection optimized by artificial bee colony algorithm. Considering the uniformity of the gray level within river object cluster and background cluster, a coarse river image segmentation is made by using the multi-threshold selection algorithm based on reciprocal gray entropy and artificial bee colony optimization; Contrapose the low convergence speed and the sensitivity to initial conditions of basic CV model, the Dirac function is replaced with the image edge intensity and the coarse segmentation results serve as the initial condition of improved CV model which is utilized to make a fine segmentation for the river image. A large number of experimental results show that, the proposed segmentation method needs not set initial conditions and has high running speed as well as segmentation accuracy.

Key words: river detection, synthetic aperture radar image segmentation, multi-threshold selection, reciprocal gray entropy, artificial bee colony optimization, Chan-Vese(CV)model

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