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Hyperspectral image classification using functional data analysis
Hong Li1; Guangrun Xiao2; Tian Xia3; Y. Y. Tang3; Luoqing Li4
2014-09
Source PublicationIEEE Transactions on Cybernetics
ISSN2168-2267
Volume44Issue:9Pages:1544 - 1555
Abstract

The large number of spectral bands acquired by hyperspectral imaging sensors allows us to better distinguish many subtle objects and materials. Unlike other classical hyperspectral image classification methods in the multivariate analysis framework, in this paper, a novel method using functional data analysis (FDA) for accurate classification of hyperspectral images has been proposed. The central idea of FDA is to treat multivariate data as continuous functions. From this perspective, the spectral curve of each pixel in the hyperspectral images is naturally viewed as a function. This can be beneficial for making full use of the abundant spectral information. The relevance between adjacent pixel elements in the hyperspectral images can also be utilized reasonably. Functional principal component analysis is applied to solve the classification problem of these functions. Experimental results on three hyperspectral images show that the proposed method can achieve higher classification accuracies in comparison to some state-of-the-art hyperspectral image classification methods.

KeywordFunctional Data Analysis (Fda) Functional Data Representation Functional Principal Component Analysis (Fpca) Hyperspectral Image Classification Support Vector Machines (Svm)
DOIhttps://doi.org/10.1109/TCYB.2013.2289331
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000342227500006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
The Source to ArticleScopus
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Citation statistics
Cited Times [WOS]:29   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorHong Li; Guangrun Xiao; Tian Xia; Y. Y. Tang; Luoqing Li
Affiliation1.School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China.
2.School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
3.Faculty of Science and Technology, University of Macau, Macau, China
4.Faculty of Mathematics and Computer Science, Hubei University, Wuhan 430062, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Hong Li,Guangrun Xiao,Tian Xia,et al. Hyperspectral image classification using functional data analysis[J]. IEEE Transactions on Cybernetics,2014,44(9):1544 - 1555.
APA Hong Li,Guangrun Xiao,Tian Xia,Y. Y. Tang,&Luoqing Li.(2014).Hyperspectral image classification using functional data analysis.IEEE Transactions on Cybernetics,44(9),1544 - 1555.
MLA Hong Li,et al."Hyperspectral image classification using functional data analysis".IEEE Transactions on Cybernetics 44.9(2014):1544 - 1555.
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