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Volume 29 Issue 3
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2021  >  29(3): 278-285
HAN Ting-ting, WANG Lu-yao, ZHOU Tian, XU Chao, ZHANG Li-hong, LI Hai-sen. Application of FCM-CV Level Set Algorithm in Sonar Image Segmentation of Small Sinking Target[J]. Journal of Unmanned Undersea Systems, 2021, 29(3): 278-285. doi: 10.11993/j.issn.2096-3920.2021.03.005
Citation: HAN Ting-ting, WANG Lu-yao, ZHOU Tian, XU Chao, ZHANG Li-hong, LI Hai-sen. Application of FCM-CV Level Set Algorithm in Sonar Image Segmentation of Small Sinking Target[J]. Journal of Unmanned Undersea Systems, 2021, 29(3): 278-285. doi: 10.11993/j.issn.2096-3920.2021.03.005
shu

Application of FCM-CV Level Set Algorithm in Sonar Image Segmentation of Small Sinking Target

doi: 10.11993/j.issn.2096-3920.2021.03.005
  • 1.

    Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China

  • 2.

    Key Laboratory of Marine Information Acquisition and SecurityHarbin Engineering University, Harbin 150001, China

  • 3.

    College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

  • Received Date: 2020-04-22
  • Rev Recd Date: 2020-09-28
  • Publish Date: 2021-06-30
    Abstract
  • Aiming at the problem that the signal-to-mix ratio of the small sinking target is low, and it is difficult to separate from the background, this study proposes a segmentation method to use the fuzzy c-means(FCM) algorithm to coo- perate with the Chan-Vese(CV) level set. This method automatically sets the initial evolution position of the level set model curve by using the membership function obtained from the FCM algorithm, which solves the problem that the CV-level set segmentation cannot obtain accurate segmentation results because of the incorrect initial position setting. Simultaneously, the control parameters of the level set evolution are estimated according to the results of fuzzy clustering, which makes the segmentation process more robust. Using outfield test and simulation data, compared with the algorithm of FCM segmentation and Markov random field segmentation, the algorithm in this study is not sensitive to speckle noise and cansegmenta complete boundary. Compared with the conventional CV-level set algorithm, the algorithm in this study can obtain more accurate segmentation results with feweriterations.

     

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    • References(12)
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