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
2016-08-01
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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
DOI10.1109/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12629
专题模式识别国家重点实验室_先进数据分析与学习
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
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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