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Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation
Ye, Zhijing1,2; Li, Hong1; Song, Yalong1; Benediktsson, Jon Atli3; Tang, Yuan Yan4
2017-02
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
Volume55Issue:2Pages:1199-1209
Abstract

This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time.

KeywordDifficultly Separable Samples (Dsss) Easily Separable Samples (Esss) Highly Confident Set Multiple 1-d Interpolation Principal Component (Pc)-based Smooth Ordering
DOIhttps://doi.org/10.1109/TGRS.2016.2621058
URLView the original
Indexed BySCIE
Language英語English
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:000392391800046
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
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Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorYe, Zhijing; Li, Hong; Song, Yalong; Benediktsson, Jon Atli; Tang, Yuan Yan
Affiliation1.School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
2.School of Science, Wuhan University of Technology, Wuhan 430070, China
3.Faculty of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland
4.Faculty of Science and Technology, University of Macau, Macau 999078, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Ye, Zhijing,Li, Hong,Song, Yalong,et al. Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,55(2):1199-1209.
APA Ye, Zhijing,Li, Hong,Song, Yalong,Benediktsson, Jon Atli,&Tang, Yuan Yan.(2017).Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation.IEEE Transactions on Geoscience and Remote Sensing,55(2),1199-1209.
MLA Ye, Zhijing,et al."Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation".IEEE Transactions on Geoscience and Remote Sensing 55.2(2017):1199-1209.
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