• DocumentCode
    2449561
  • Title

    A Novel Unsupervised Iteration Classification Method For Fully Polarimetric SAR Image

  • Author

    Lei, Yang ; Jun, Zhao Yong ; Gang, Wang Zhi

  • Author_Institution
    Inst. of Inf. Eng., Inf. Eng. Univ., Zhengzhou
  • fYear
    2006
  • fDate
    26-29 Oct. 2006
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Based on the theory of eigen-decomposition of fully polarimetric synthetic aperture radar (SAR) and scatter criterion clustering method, an unsupervised iteration classification method is proposed. It is simple, flexible and with high precision. Several schemes of the iteration classification method are given in this paper. The characteristic of each scheme is carefully analyzed according to the properties of eigen-decomposition and scatter criterion, and each scheme holds its own merit. Experiments are done on SIR-C/X-SAR data near Tien Mountains and perfect classification results are obtained. More importantly, this method is robust and has high adaptability.
  • Keywords
    eigenvalues and eigenfunctions; image classification; iterative methods; pattern clustering; radar imaging; synthetic aperture radar; SIR-C/X-SAR data; Tien Mountains; eigendecomposition; fully polarimetric SAR image; scatter criterion clustering method; synthetic aperture radar; unsupervised iteration classification method; Clustering methods; Entropy; Image analysis; Information analysis; Interferometry; Layout; Matrix decomposition; Radar scattering; Robustness; Synthetic aperture radar; Unsupervised classification; eigen-decomposition; fully polarimetic SAR; scatter criterion;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Antennas, Propagation & EM Theory, 2006. ISAPE '06. 7th International Symposium on
  • Conference_Location
    Guilin
  • Print_ISBN
    1-4244-0162-3
  • Electronic_ISBN
    1-4244-0163-1
  • Type

    conf

  • DOI
    10.1109/ISAPE.2006.353512
  • Filename
    4168173