• DocumentCode
    2895217
  • Title

    SAR Image Classification Combining Structural and Statistical Methods

  • Author

    Tankam, Narcisse Talla ; Dipanda, Albert ; Fotsing, Janvier ; Tonye, Emmanuel

  • Author_Institution
    Comput. Sci. Dept., Univ. of Dschang (IUTFV-UDs), Bandjoun, Cameroon
  • fYear
    2011
  • fDate
    Nov. 28 2011-Dec. 1 2011
  • Firstpage
    468
  • Lastpage
    475
  • Abstract
    The main objective of this paper is to develop a new technique of SAR image classification. This technique combines structural parameters, including the Sill, the slope, the fractal dimension and the range, with statistical methods in a supervised image classification. Thanks to the range parameter, we define the suitable size of the image window used in the proposed approach of supervised image classification. This approach is based on a new way of characterising different classes identified on the image. The first step consists in determining relevant area of interest. The second step consists in characterising each area identified, by a matrix. The last step consists in automating the process for image classification.
  • Keywords
    fractals; image classification; learning (artificial intelligence); statistical analysis; SAR image classification; fractal dimension; image window; statistical methods; structural method; supervised image classification; Educational institutions; Equations; Fractals; Image classification; Mathematical model; Statistical analysis; Structural engineering; SAR image; structural parameter; supervised classification; variogram;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal-Image Technology and Internet-Based Systems (SITIS), 2011 Seventh International Conference on
  • Conference_Location
    Dijon
  • Print_ISBN
    978-1-4673-0431-3
  • Type

    conf

  • DOI
    10.1109/SITIS.2011.13
  • Filename
    6120689