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《Acta Geodaetica et Cartographica Sinica》 2009-01
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Study of the Comparison of Land Cover Classification for Multitemporal MODIS Images

GUO Jian1,2,ZHANG Ji-xian1,ZHANG Yong-hong1,CAO Yin-xuan1 1.Institute of Photogrammetry and Remote Sensing,Chinese Academy of Surveying and Mapping,Beijing 100039,China;2.School of Geomatics,Liaoning Technical University,Fuxin 123000,China  
The four broadly used classification methods,which are Maximum Likelihood Classification(MLC),Self-Organized Neural Network(SONN),Support Vector Machine(SVM)and Decision Tree Classification(DTC),are firstly applied for the land cover remote sensing classification based on the multitemporal Moderate Resolution Imaging Spectroradiometer(MODIS)images of Heilongjiang area in this paper.Then,this paper deeply compares the four classifiers through different aspects,as a result,DTC is the best and MLC as one of the classical methods is more stable than other three methods.Therefore,the land cover classification over China is made by DTC and MLC,afterwards compare them again.The conclusions in this paper are valuable for how to select classifiers in the similar applications.
【Fund】: 国家测绘局资助项目“全球测图二期工程(中国部分)”(1469990624205-2)
【CateGory Index】: P237
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