• DocumentCode
    2662642
  • Title

    Analysis of non-Gaussian POLSAR data

  • Author

    Doulgeris, Anthony ; Anfinsen, Stian Normann ; Eltoft, Torbjørn

  • Author_Institution
    Tromso Univ., Tromso
  • fYear
    2007
  • fDate
    23-28 July 2007
  • Firstpage
    160
  • Lastpage
    163
  • Abstract
    In this paper we present a generalised Wishart classifier derived from a non-Gaussian model for polarimetric synthetic aperture radar (POLSAR) data. Our starting point is to demonstrate that the scale mixture of Gaussian (SMoG) distribution model is suitable for modelling POLSAR data. We show that the distribution of the sample covariance matrix for the SMoG model is given as a generalisation of the Wishart distribution, and present this expression in integral form. We then derive the closed form solution for one particular SMoG distribution, known as the multivariate K-distribution. Based on this new distribution, termed the K-Wishart distribution, we propose a Bayesian classification scheme, which can be used in both supervised and unsupervised mode. Modelling and classification is tested on airborne EMISAR data.
  • Keywords
    Bayes methods; Gaussian distribution; covariance matrices; geophysical techniques; image classification; radar polarimetry; synthetic aperture radar; Bayesian classification scheme; K-Wishart distribution; POLSAR data; SMoG distribution model; airborne EMISAR data; generalised Wishart classifier; multivariate K-distribution; nonGaussian model; polarimetric synthetic aperture radar data; sample covariance matrix distribution; scale mixture of Gaussian distribution model; supervised mode; unsupervised mode; Bayesian methods; Classification algorithms; Closed-form solution; Context modeling; Covariance matrix; Polarization; Radar scattering; Random variables; Shape; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-1211-2
  • Electronic_ISBN
    978-1-4244-1212-9
  • Type

    conf

  • DOI
    10.1109/IGARSS.2007.4422754
  • Filename
    4422754