Comparison of the land cover classification methods based on single-temporal MODIS data
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DOI：10.7606/j.issn.1000-7601.2008.03.50
Key Words: MODIS  maximum likelihood classifier  BP neural network  decision tree classifier  See 5．0  land cover classification
Author NameAffiliation
XU Xiaotao Key Laboratory of Western China’s Environmental Systems （Ministry of Education） Lanzhou University，Lanzhou，Gansu 730000，China 
HAN Tao Institute of Arid Meteorology CMA Lanzhou Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu GOV and CMA， Lanzhou， Gansu 730020， China 
XIE Yaowen Key Laboratory of Western China’s Environmental Systems （Ministry of Education） Lanzhou University，Lanzhou，Gansu 730000，China 
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Abstract:
      Based on single-temporal MODIS data of Gansu Province mainly using its visible spectra three classifiers－the maximum likelihood BP neural network and decision tree based on data mining software of See 5．0 are used for land cover classification research．The validated result shows that the decision tree algorithm has the best performance of extraction with an overall accuracy of 82．13％ followed by the BP network algorithm and the maximum likelihood classifier has the worst performance．Data mining software of See 5．0 with boosting technique can build decision tree quickly and improve the precision of miscible classes．
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