• DocumentCode
    2683689
  • Title

    Smoothing SAR images with neural networks

  • Author

    Ellis, John ; Warner, Martin ; White, Richard G.

  • Author_Institution
    NA Software Ltd., Liverpool, UK
  • Volume
    4
  • fYear
    1994
  • fDate
    8-12 Aug 1994
  • Firstpage
    1883
  • Abstract
    Describe an approach, to the removal of radar image noise, based on the use of neural networks. A neural network factorisation scheme, based on the use of vector quantisers, allows the authors to produce a more effective solution than that which is possible with a single network. The factorised neural network is currently trained to learn the smoothing behaviour of a noise removal algorithm. The success of the approach demonstrates the potential for this technique and opens the way for its use in learning a true noise smoothing mapping based on the comparison of single and multi look radar data
  • Keywords
    geophysical signal processing; geophysical techniques; image enhancement; multilayer perceptrons; radar applications; radar imaging; remote sensing; remote sensing by radar; smoothing methods; spaceborne radar; synthetic aperture radar; SAR; factorisation scheme; geophysical measurement technique; image noise removal; image processing; land surface; learning; multilayer perceptron; neural net; neural network; noise removal algorithm; radar imaging; remote sensing; smoothing; synthetic aperture radar; terrain mapping; vector quantiser; Airborne radar; Filters; Neural networks; Radar cross section; Radar imaging; Radar remote sensing; Scattering; Smoothing methods; Speckle; Synthetic aperture radar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International
  • Conference_Location
    Pasadena, CA
  • Print_ISBN
    0-7803-1497-2
  • Type

    conf

  • DOI
    10.1109/IGARSS.1994.399601
  • Filename
    399601