• DocumentCode
    3065545
  • Title

    Nonlinear Bayesian unmixing of geospatial data based on GIBBS sampling

  • Author

    Ryuei Nishii ; Pan Qin ; Uchi, Daisuke

  • Author_Institution
    Inst. of Math. for Ind., Kyushu Univ., Fukuoka, Japan
  • fYear
    2013
  • fDate
    21-26 July 2013
  • Firstpage
    3104
  • Lastpage
    3107
  • Abstract
    Image classification has a long history for estimating landcover categories by feature vectors, and various methods have been proposed from many viewpoints; statistics, machine learning and others. Multivariate normal distributions are frequently used to model feature distributions. Also, it is known that contextual classification methods based on Markov random fields (MRF) improve non-contextual classifiers successfully.
  • Keywords
    geophysical image processing; geophysical techniques; image classification; land cover; Gibbs sampling; Markov random fields; feature vectors; geospatial data; image classification; land cover categories; machine learning; model feature distributions; multivariate normal distributions; noncontextual classifiers; nonlinear Bayesian unmixing; Equations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
  • Conference_Location
    Melbourne, VIC
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4799-1114-1
  • Type

    conf

  • DOI
    10.1109/IGARSS.2013.6723483
  • Filename
    6723483
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3065545