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

doi:10.1109/TGRS.2016.2542342

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	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
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2016-08-01
卷号	54 期号:8 页码:4461-4478
文章类型	Article
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.
关键词	Binary Codes Classification Feature Fusion Hashing Hyperspectral Images
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/TGRS.2016.2542342
关键词[WOS]	GRAY-LEVEL COOCCURRENCE ; FEATURE-SELECTION ; FEATURE-EXTRACTION ; DECISION TREES ; URBAN AREAS ; LIDAR DATA ; PROFILES ; REGRESSION ; KERNELS ; MATRIX
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61573352 ; Beijing Natural Science Foundation(4142057) ; 61472119 ; 91338202 ; 91438105)
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000381434600008
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12629
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
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
第一作者单位	模式识别国家重点实验室
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.

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