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	An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery
	Huang, Xin; Zhang, Liangpei
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2013
卷号	51期号:1
关键词	Classification feature extraction high resolution morphological multifeature object-based semantic support vector machines (SVMs) WorldView-2
ISSN号	0196-2892
DOI	10.1109/TGRS.2012.2202912
URL标识	查看原文
收录类别	SCIE ; EI ; ESI高被引论文
语种	英语
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/4119349
专题	武汉大学
推荐引用方式 GB/T 7714	Huang, Xin,Zhang, Liangpei. An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2013,51(1).
APA	Huang, Xin,&Zhang, Liangpei.(2013).An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,51(1).
MLA	Huang, Xin,et al."An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 51.1(2013).

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