• DocumentCode
    2098865
  • Title

    SAR Image Segmentation Method Using DP Mixture Models

  • Author

    Li, Sun ; Yanning, Zhang ; Miao, Ma ; Guangjian, Tian

  • Author_Institution
    Sch. of Comput. Sci., Northwestern Polytech. Univ., Xian, China
  • Volume
    2
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    598
  • Lastpage
    601
  • Abstract
    This paper presents a new method for segmentation of synthetic aperture radar (SAR) images. Based on a non-parametric Bayesian infinite mixture model, Dirichlet process mixture model cluster method is proposed to segment SAR image. The traditional finite mixture model segmentation method is adapted extensively in SAR image segmentation, but the performance and the robustness is not good enough. However, the proposed infinite mixture model can simulate the intrinsic property of SAR image and the segmentation method can determine the cluster number automatically. The experiment results on the simulated data and real data show that the proposed method gets comparative performance and robustness with the traditional methods.
  • Keywords
    Bayes methods; boundary-value problems; image segmentation; radar imaging; synthetic aperture radar; DP mixture models; Dirichlet process mixture model cluster method; SAR image segmentation; nonparametric Bayesian infinite mixture model; synthetic aperture radar; Bayesian methods; Clustering algorithms; Computer science; Image segmentation; Optical filters; Optical noise; Parameter estimation; Robustness; Speckle; Synthetic aperture radar; Dirichlet process mixture model; Non-parametric Bayesian model; SAR image segmentation; infinite mixture model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3746-7
  • Type

    conf

  • DOI
    10.1109/ISCSCT.2008.20
  • Filename
    4731695