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Remote Sensing Technology and Application  2009, Vol. 24 Issue (5): 648-653    DOI: 10.11873/j.issn.1004-0323.2009.5.648
    
The Study of the Northwest Arid Zone Land-Cover Classification Based on C5.0 Decision Tree Algorithm at Wuwei City,Gansu Province
 QI Hong-chao,QI Yuan,XU Zhen
730000Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy ofSciences,Lanzhou 730000,China
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Abstract  

In the broadly northwest arid regions,frequently,same object has different spectral characters because of the special characteristics of land cover change such as complex causes of formation,sensitivity to environment change,rapid and violent change and obvious differences in landscape.The conventional methods of classification including visual interpretation,supervised classification,unsupervised classification,and artificial decision tree classification have disadvantages in the efficiency or the accuracy.In this paper,machine learning algorithm based on C5.0 decision tree was used to classify the entire study area automatically according to the sample data mining classification rules.Spectral features,NDVI,TC,texture and other informations were involved in the algorithm.More classification rules could be mined by machine learning decision tree.C5.0 algorithm handling with both continuous and discrete data is independent of the distribution of sampling sites,The classification rules mined by this algorithm were interpretable.Other superiority of this algorithm included the fast speed of training and higher accuracy than many other classifiers.Thus,it is able to be used in the mapping of land use/cover change in a large scale in northwest arid regions.

 

Key words:  C5.0 algorithm       The northwest arid region       Land cover       See5.0       NLCD     
Received:  27 May 2009      Published:  24 August 2010
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QI Hong-Chao
QI Yuan
XU Tian

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QI Hong-Chao, QI Yuan, XU Tian. The Study of the Northwest Arid Zone Land-Cover Classification Based on C5.0 Decision Tree Algorithm at Wuwei City,Gansu Province. Remote Sensing Technology and Application, 2009, 24(5): 648-653.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2009.5.648     OR     http://www.rsta.ac.cn/EN/Y2009/V24/I5/648

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