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	Decision tree learning for habitat landscape classification
	Zhang Shuang ; Liu Xuehua ; Jin Qiang
	2010-05-10 ; 2010-05-10
关键词	Practical/ data mining decision trees expert systems geographic information systems image classification learning (artificial intelligence) remote sensing/ decision tree learning habitat landscape classification landscape classification knowledge data mining ground-truthing data remote sensing digital elevation data set Qinling Mountains C5.0 algorithm knowledge accuracy classification accuracy geographical information system expert system/ C7840 Geography and cartography computing C5260B Computer vision and image processing techniques C6170K Knowledge engineering techniques C1160 Combinatorial mathematics
中文摘要	A landscape classification knowledge was mined from ground-truthing data using decision tree learning for knowledge-based habitat landscape classification. 750 field samples were collected from a remote sensing and digital elevation data set within a 100-km/sup 2/ research area on the southern slope of the Qinling Mountains. The expert classification rules were defined using the C5.0 algorithm for the knowledge-based classification. The influence of different numbers of sample points on the knowledge accuracy was then evaluated. The results show that the knowledge can be conveniently mined using decision tree learning with a sufficient number of sample points. The classification accuracy increases as the number of sample points increases. The decision tree learning classification gave a highest accuracy of 79.0%.
语种	中文 ; 中文
出版者	Tsinghua Univ. Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/23506]
专题	清华大学
推荐引用方式 GB/T 7714	Zhang Shuang,Liu Xuehua,Jin Qiang. Decision tree learning for habitat landscape classification[J],2010, 2010.
APA	Zhang Shuang,Liu Xuehua,&Jin Qiang.(2010).Decision tree learning for habitat landscape classification..
MLA	Zhang Shuang,et al."Decision tree learning for habitat landscape classification".(2010).

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