Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study

doi:10.1109/TGRS.2016.2542342

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 遥感图像处理团队

	Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study
	Zhong, Zisha; Fan, Bin; Ding, Kun; Li, Haichang; Xiang, Shiming; Pan, Chunhong; zszhong@nlpr.ia.ac.cn
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2016-08-01
卷号	54 期号:8 页码:4461-4478
关键词	Binary Codes Classification Feature Fusion Hashing Hyperspectral Images
DOI	10.1109/TGRS.2016.2542342
文献子类	Article
英文摘要	Due to the complementary properties of different features, multiple feature fusion has a large potential for hyperspectral imagery classification. At the meantime, hashing is promising in representing a high-dimensional float-type feature with extremely low bit binary codes while maintaining the performance. In this paper, we study the possibility of using hashing to fuse multiple features for hyperspectral imagery classification. For this purpose, we propose a multiple feature fusion framework to evaluate the performance of using different hashing methods. For comparison and completeness, we also have an extensive comparison to five subspace-based dimension reduction methods and six fusion-based methods which are popular solutions to deal with multiple features in hyperspectral image classification. Experimental results on four benchmark hyperspectral data sets demonstrate that using hashing to fuse multiple features can achieve comparable or better performance with the traditional subspace-based dimension reduction methods and fusion-based methods. Moreover, the binary features obtained by using hashing need much less storage and are faster to compute distances with the help of machine instructions.
WOS关键词	GRAY-LEVEL COOCCURRENCE ; FEATURE-SELECTION ; FEATURE-EXTRACTION ; DECISION TREES ; URBAN AREAS ; LIDAR DATA ; PROFILES ; REGRESSION ; KERNELS ; MATRIX
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000381434600008
资助机构	National Natural Science Foundation of China(61573352 ; Beijing Natural Science Foundation(4142057) ; 61472119 ; 91338202 ; 91438105)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12629]
专题	自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者	zszhong@nlpr.ia.ac.cn
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhong, Zisha,Fan, Bin,Ding, Kun,et al. Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2016,54(8):4461-4478.
APA	Zhong, Zisha.,Fan, Bin.,Ding, Kun.,Li, Haichang.,Xiang, Shiming.,...&zszhong@nlpr.ia.ac.cn.(2016).Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,54(8),4461-4478.
MLA	Zhong, Zisha,et al."Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 54.8(2016):4461-4478.