Volume 36 Issue 3
Mar.  2011
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LI Zuchuan, MA Jianwen, ZHANG Rui, LI Liwei. Classifying Hyperspectral Data Using Support Vector Machine Conditional Random Field[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3): 306-310.
Citation: LI Zuchuan, MA Jianwen, ZHANG Rui, LI Liwei. Classifying Hyperspectral Data Using Support Vector Machine Conditional Random Field[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3): 306-310.
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Classifying Hyperspectral Data Using Support Vector Machine Conditional Random Field

  • 1 Center for Earth Observation and Digital Earth,Chinese Academy of Sciences,9 Beiyitiao,Zhongguancun,Beijing 100190,China;2 Graduate University of Chinese Academy of Sciences,A19 Yuquan Road,Beijing 100049,China;3 Institute of Remote Sensing Applications,Chinese Academy of Sciences,A20 Datun Road,Beijing 100101,China

Funds: 国家自然科学基金资助项目(40901234)；中国科学院知识创新工程青年人才领域前沿项目专项资助项目(O8S01100CX)
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  • Received Date: January 26, 2011
  • Published Date: March 04, 2011
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    • Abstract
  • With there problems at hands,an improved random field,support vector machine conditional random field(SVM-CRF) was proposed.It uses SVM as its unary potential,combining the merits of SVM and CRF.Experiments using AVIRIS hyperspectral data as input were carried out,and SVM-CRF was analyzed extensively.Experimental results show that SVM-CRF is superior to SVM and classic CRF in terms of classification accuracies.
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