• DocumentCode
    3366147
  • Title

    Image distance based ship detection using SAR images

  • Author

    Bo, Hua ; Ma, Fulong

  • Author_Institution
    Inf. Eng. Dept., Shanghai Maritime Univ., Shanghai
  • fYear
    2009
  • fDate
    26-29 March 2009
  • Firstpage
    675
  • Lastpage
    678
  • Abstract
    A novel method is developed for ship detection in synthetic aperture radar (SRA) images, which is based on image distance computation techniques. Using a second-order hidden Markov mesh model to learn statistical models of images, one can obtain the distance of two images for the purpose of detecting ships. First, the features of an image can be extracted using a method that best matches its statistical model, which is related to dynamic programming. Second, given the state transition matrix and observation distributions within states, statistical distance between images based on the similarity of their statistical models can be estimated. Experimental results demonstrate that this ship detection algorithm can effectively enhance ship target as well as suppress speckle and has better detection precision and lower calculation complexity.
  • Keywords
    feature extraction; hidden Markov models; mesh generation; radar target recognition; ships; speckle; statistical analysis; synthetic aperture radar; SAR images; detection precision; dynamic programming; feature extraction; image distance computation; second-order hidden Markov mesh model; ship detection; speckle suppression; state transition matrix; statistical models; synthetic aperture radar; Change detection algorithms; Condition monitoring; Detection algorithms; Hidden Markov models; Marine vehicles; Object detection; Radar detection; Random variables; Speckle; Synthetic aperture radar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networking, Sensing and Control, 2009. ICNSC '09. International Conference on
  • Conference_Location
    Okayama
  • Print_ISBN
    978-1-4244-3491-6
  • Electronic_ISBN
    978-1-4244-3492-3
  • Type

    conf

  • DOI
    10.1109/ICNSC.2009.4919358
  • Filename
    4919358