• DocumentCode
    476302
  • Title

    Modeling multisource remote sensing image classifier based on the MDL principle: Theoretical aspects

  • Author

    Yin, Qian ; Guo, Ping ; Yuan, Zhi-Yong ; Wei, Zu-kuan ; Zeng, Wen-yi

  • Author_Institution
    Image Process. & Pattern Recognition Lab., Beijing Normal Univ., Beijing
  • Volume
    6
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    3497
  • Lastpage
    3502
  • Abstract
    A theoretical study for modeling technique of the remote sensing image classification based on the minimum description length (MDL) principle is presented in the paper. According to the MDL principle, modeling problem is an optimization procedure to find the shortest expected code length. Kullback-Leibler (KL) divergence is adopted as the system cost function to measure expected codelength, and the codelength will be the model we desired. The advantage of using the MDL principle to build appropriate model is analyzed theoretically, model optimization technique also is described.
  • Keywords
    geophysical signal processing; image classification; remote sensing; Kullback-Leibler divergence; MDL principle; minimum description length; multisource remote sensing image classifier; optimization technique; shortest expected code length; Atmospheric modeling; Context modeling; Cybernetics; Feature extraction; Image classification; Image recognition; Machine learning; Power system modeling; Remote monitoring; Remote sensing; Minimum Description Length; classification technique; model optimization; remote sensing image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4621009
  • Filename
    4621009