UM
A Manifold Alignment Approach for Hyperspectral Image Visualization with Natural Color
Danping Liao1; Yuntao Qian1; Jun Zhou2; Yuan Yan Tang3
2016-06-01
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
Volume54Issue:6Pages:3151-3162
Abstract

The trichromatic visualization of hundreds of bands in a hyperspectral image (HSI) has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing an HSI with natural color by the fusion of an HSI and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high-dimensional spectral space of the HSI and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the HSI. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the HSI and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other HSIs acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user-specified bands as natural color images, which is very helpful for users to scan bands of interest.

KeywordHyperspectral Image (Hsi) Image Fusion Manifold Alignment Visualization
DOIhttps://doi.org/10.1109/TGRS.2015.2512659
URLView the original
Indexed BySCIE
Language英语
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:000377477100005
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Fulltext Access
Citation statistics
Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorYuntao Qian
Affiliation1.College of Computer Science, Zhejiang University, Hangzhou 310027, China
2.School of Information and Communication Technology, Griffith University, Nathan, Qld. 4111, Australia
3.Faculty of Science and Technology, University of Macau, Macau 999078, China
Recommended Citation
GB/T 7714
Danping Liao,Yuntao Qian,Jun Zhou,et al. A Manifold Alignment Approach for Hyperspectral Image Visualization with Natural Color[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(6):3151-3162.
APA Danping Liao,Yuntao Qian,Jun Zhou,&Yuan Yan Tang.(2016).A Manifold Alignment Approach for Hyperspectral Image Visualization with Natural Color.IEEE Transactions on Geoscience and Remote Sensing,54(6),3151-3162.
MLA Danping Liao,et al."A Manifold Alignment Approach for Hyperspectral Image Visualization with Natural Color".IEEE Transactions on Geoscience and Remote Sensing 54.6(2016):3151-3162.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Danping Liao]'s Articles
[Yuntao Qian]'s Articles
[Jun Zhou]'s Articles
Baidu academic
Similar articles in Baidu academic
[Danping Liao]'s Articles
[Yuntao Qian]'s Articles
[Jun Zhou]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Danping Liao]'s Articles
[Yuntao Qian]'s Articles
[Jun Zhou]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.