• DocumentCode
    3069739
  • Title

    Three-dimensional geometrical feature estimation for ship classification through SAR images

  • Author

    Chongwen Duan ; Weidong Hu ; Xiaoyong Du

  • Author_Institution
    ATR Key Lab., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2012
  • fDate
    27-29 Sept. 2012
  • Firstpage
    181
  • Lastpage
    184
  • Abstract
    For the purpose of ship classification with SAR images, we put forward a way to extract the three-dimensional geometrical features of the ship in the case when the extracting and matching of the ship scattering centers are handicapped by the ocean movement and the scintillation of SAR images. An ellipsoid-ellipse model is used to approximate the ship and its SAR silhouette. With this model, the radar azimuths and the ship length are estimated in an almost unbiased way. Moreover, by introducing the length-width joint probability density function based on real samples, we got the width and height estimation by solving a constrained nonlinear Least Square problem. The estimations show satisfactory accuracy and robustness for the classification. The electromagnetic simulated images are used to validate the feasibility of the ellipsoid-ellipse model, as well as the efficiency of the algorithm.
  • Keywords
    feature extraction; image classification; radar imaging; ships; synthetic aperture radar; SAR images; SAR silhouette; constrained nonlinear least square problem; electromagnetic simulated images; radar azimuths; scintillation; ship classification; ship length; ship scattering centers; three-dimensional geometrical feature estimation; Azimuth; Estimation; Feature extraction; Marine vehicles; Radar imaging; Synthetic aperture radar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-1976-8
  • Type

    conf

  • DOI
    10.1109/ICIAFS.2012.6419901
  • Filename
    6419901