• DocumentCode
    2196631
  • Title

    Application Research of Support Vector Machine in Multi-Spectra Remote Sensing Image Classification

  • Author

    Wang, Yujian ; Yuan, Jiazheng ; Fan, Lili ; Liu, Zhiguo

  • Author_Institution
    Inst. of Inf. Technol., Beijing Union Univ., Beijing, China
  • fYear
    2009
  • fDate
    17-19 Oct. 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In order to improve the accuracy of multi-spectra remote sensing image classification, a terrain classification method based on support vector machine is proposed. A remote sensing image classification method based on SVM algorithm of C-SVC type is introduced and emphasis is put on the study of the improved SMO algorithm. In order to improve efficiency of classification, multiple-spectra remote sensing image classification of terrain is classified using fuzzy clustering based on fuzzy c-means algorithm which adopt semi-supervised improved algorithm. The experimental results show that the approach has an advantage over traditional classification methods.
  • Keywords
    fuzzy set theory; geophysical signal processing; image classification; learning (artificial intelligence); pattern clustering; support vector machines; terrain mapping; SVM algorithm; fuzzy c-means algorithm; fuzzy clustering; improved SMO algorithm; multispectra remote sensing image classification; semisupervised improved algorithm; support vector machine; terrain classification method; Clustering algorithms; Image classification; Information technology; Pattern recognition; Remote sensing; Risk management; Satellites; Space technology; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-4129-7
  • Electronic_ISBN
    978-1-4244-4131-0
  • Type

    conf

  • DOI
    10.1109/CISP.2009.5305618
  • Filename
    5305618