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OILSEED RAPE PLANTING AREA EXTRACTION BY SUPPORT VECTOR MACHINE USING LANDSAT TM DATA

  • Yuan Wang
  • Jingfeng Huang
  • Xiuzhen Wang
  • Fumin Wang
  • Zhanyu Liu
  • Junfeng Xu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)

Abstract

One parametric classify (Maximum likelihood classify, MLC for short) and two non-parametric classifiers (Adaptive resonance theory mappings and Support vector machines, ARTMAP and SVM for short) were presented in this study. Base on the confusion matrix and the pixels fuzzy analysis, the nonparametric classifier may be a more preferable approach than the parametric classifier for some remote sensing applications and deserves further investigation. The ARTMAP classify represent much best than the rest of classify, especially for the grade of pure pixel (90-100% pureness), Kappa coefficients and overall accuracy were nearly 100%. The higher pureness the pixels were, the better classification accuracy was got.

Keywords

Support Vector Machine Classification Accuracy Kappa Coefficient Confusion Matrix Parametric Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Yuan Wang
    • 1
  • Jingfeng Huang
    • 1
  • Xiuzhen Wang
    • 2
  • Fumin Wang
    • 1
  • Zhanyu Liu
    • 1
  • Junfeng Xu
    • 1
  1. 1.Institute of Agricultural Remote Sensing and Information Technology, Zhejiang UniversityHangzhouChina
  2. 2.Zhejiang Meteorological InstituteHangzhouChina

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