• DocumentCode
    2981961
  • Title

    Unsupervised polarimetric SAR image classification using fisher linear discriminant

  • Author

    Chen, Bo ; Wang, Shuang ; Jiao, Licheng ; Zhang, Shuang

  • Author_Institution
    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´´an, China
  • fYear
    2009
  • fDate
    26-30 Oct. 2009
  • Firstpage
    738
  • Lastpage
    741
  • Abstract
    How to use the POLSAR data to classify and interpret the conditions of the earth is a very important research field of POLSAR. In this paper, we propose an improved algorithm on the basis of studying and analyzing some common algorithms. This technique introduces the fisher criterion in the feature selection and the hierarchical method in the classification of POLSAR image, which can improve Lee´s combination method of the unsupervised classification based on polarimetric target decomposition and the maximum likelihood classifier based on the complex Wishart distribution. The effectiveness of this algorithm is demonstrated using a polerimetric SAR image.
  • Keywords
    image classification; radar polarimetry; synthetic aperture radar; Fisher linear discriminant; Lee combination method; POLSAR image; Wishart distribution; feature selection; hierarchical method; maximum likelihood classifier; polarimetric target decomposition; unsupervised polarimetric SAR image classification; Algorithm design and analysis; Earth; Eigenvalues and eigenfunctions; Entropy; Image classification; Image processing; Information processing; Laboratories; Oceans; Radar scattering; Radar polarimetry; feature selection; synthetic aperture radar; terrain classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Synthetic Aperture Radar, 2009. APSAR 2009. 2nd Asian-Pacific Conference on
  • Conference_Location
    Xian, Shanxi
  • Print_ISBN
    978-1-4244-2731-4
  • Electronic_ISBN
    978-1-4244-2732-1
  • Type

    conf

  • DOI
    10.1109/APSAR.2009.5374217
  • Filename
    5374217