Volume 30 Issue 8
Aug.  2005
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XU Fang, MEI Wensheng, ZHANG Zhihua. Least Squares Support Vector Machines for Aerial Images Segmentation[J]. Geomatics and Information Science of Wuhan University, 2005, 30(8): 694-698.
Citation: XU Fang, MEI Wensheng, ZHANG Zhihua. Least Squares Support Vector Machines for Aerial Images Segmentation[J]. Geomatics and Information Science of Wuhan University, 2005, 30(8): 694-698.
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Least Squares Support Vector Machines for Aerial Images Segmentation

  • 1School of Geodesy and Geomatics, Wuhan University,129 Luoyu Road, Wuhan 430079, China;2Qingdao Institute of Exploration and Surveying, 2 Renmin Road, Qingdao 266033,China

Funds: 国家自然科学基金资助项目(40271094)
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  • Received Date: May 07, 2005
  • Revised Date: May 07, 2005
  • Published Date: August 04, 2005
    Graphical Abstract
    • Abstract
  • SVM has desirable classification ability even if with fewer samples. In addition, LS-SVM reduces the complexity further through replacing the inequality of SVM by equality. This paper applies LS-SVM to aerial images segmentation. This paper researches on the different kernel and sparse of LS-SVM. The kernel influences aerial image segmentation. The briefness of decision function is reached by the LS-SVM sparseness. The experiments show the segmentation results of LS-SVM are better.
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