• DocumentCode
    1327725
  • Title

    Ship wake-detection procedure using conjugate gradient trained artificial neural networks

  • Author

    Fitch, J.P. ; Lehman, S.K. ; Dowla, F.U. ; Lu, S.Y. ; Johansson, E.M. ; Goodman, D.M.

  • Author_Institution
    Lawrence Livermore Nat. Lab., California Univ., CA, USA
  • Volume
    29
  • Issue
    5
  • fYear
    1991
  • fDate
    9/1/1991 12:00:00 AM
  • Firstpage
    718
  • Lastpage
    726
  • Abstract
    A method has been developed to reduce large two-dimensional images to significantly smaller feature lists. These feature lists overcome the problem of storing and manipulating large amounts of data. A new artificial neural network using conjugate gradient training methods, operating on sets of feature lists, was successfully trained to determine the presence or absence of wakes in synthetic aperture radar images. A comparison has been made between the different conjugate gradient and steepest-descent training methods and has demonstrated the superiority of the former over the latter
  • Keywords
    computerised pattern recognition; computerised picture processing; conjugate gradient methods; geophysics computing; neural nets; ocean waves; oceanographic techniques; remote sensing by radar; conjugate gradient trained artificial neural networks; feature lists; large two-dimensional images; ocean waves; remote sensing; ships; synthetic aperture radar images; wake-detection procedure; Artificial neural networks; Marine vehicles; Ocean temperature; Pattern recognition; Pixel; Radar detection; Radar imaging; Sea surface; Spaceborne radar; Surface waves;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/36.83986
  • Filename
    83986