Bayesian filtering and supervised classification in image remote sensing

Jean-Paul Rasson, Vincent Granville

    Research output: Contribution to journalArticle


    In the framework of image remote sensing, Markov random fields are used to model the distribution of points both in the 2-dimensional geometrical layout of the image and in the spectral grid. The problems of image filtering and supervised classification are investigated. The mixture model of noise developed here and appropriate Gibbs densities yield a same approach and a same efficient ICM algorithm both for filtering and classifying.
    Original languageEnglish
    Pages (from-to)203-225
    Number of pages23
    JournalComputational Statistics and Data Analysis
    Issue number2
    Publication statusPublished - 1995


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