Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Multivariate Versus Univariate Decision Trees
Multivariate Versus Univariate Decision Trees
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
End user friendly data mining with decision trees: a reality or a wish?
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Using NDVI to define thermal south in several mountainous landscapes of California
Computers & Geosciences
A hierarchical object oriented method for land cover classification of SPOT 5 imagery
WSEAS Transactions on Information Science and Applications
WSEAS Transactions on Information Science and Applications
Evolutionary flexible neural networks for intrusion detection system
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Improving the prediction accuracy of liver disorder disease with oversampling
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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The traditional method of application of remote sensing data for land cover mapping is the use of supervised classification and unsupervised classification. Decision tree, showing great advantages in remote sensing classification, is computationally fast, makes no statistical assumptions, and can handle data that are represented on different measurement scales. Decision tree classification has been successfully applied to many classification problems, but rarely applied to mapping of wetlands. In this study, decision tree was proposed to extract wetland from Landsat 5/Thematic Mapper (TM) imageries in a wide area of Yinchuan plain. Tasseled Cap (TC) transformation was used to identity the different wetland types and normalized difference vegetation index (NDVI) was computed to distinguish paddy wetland and lake wetland. Results from this analysis show that the decision tree has an outstanding performance compared with the supervised classification in maximum likelihood method. The overall accuracy of supervised classification is 64.60%, while that of decision tree classification was 83.80%. Besides, it appears that a decision tree combinations different useful knowledge is an effective and promising classification method.