CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
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
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
2016-08-01
Volume54Issue:8Pages:4461-4478
SubtypeArticle
AbstractDue 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.
KeywordBinary Codes Classification Feature Fusion Hashing Hyperspectral Images
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2016.2542342
WOS KeywordGRAY-LEVEL COOCCURRENCE ; FEATURE-SELECTION ; FEATURE-EXTRACTION ; DECISION TREES ; URBAN AREAS ; LIDAR DATA ; PROFILES ; REGRESSION ; KERNELS ; MATRIX
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61573352 ; Beijing Natural Science Foundation(4142057) ; 61472119 ; 91338202 ; 91438105)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000381434600008
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12629
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding Authorzszhong@nlpr.ia.ac.cn
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
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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