Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification (vol 55, pg 1544, 2017)

doi:10.1109/TGRS.2018.2789418

Residential College	false
Status	已發表Published
	Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification (vol 55, pg 1544, 2017)
	Ye, Minchao; Qian, Yuntao; Zhou, Jun; Tang, Yuan Yan
	2018-05
Source Publication	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
Volume	56 Issue:5 Pages:3002-3003
DOI	10.1109/TGRS.2018.2789418
URL	View the original
Indexed By	SCIE
Language	英語English
WOS Research Area	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS Subject	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS ID	WOS:000430739600040
Publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to Article	WOS
Scopus ID	2-s2.0-85041669167
Fulltext Access	View Full-Text via DOI View Full-Text via Web of Science
Citation statistics
Document Type	Journal article
Collection	University of Macau
Recommended Citation GB/T 7714	Ye, Minchao,Qian, Yuntao,Zhou, Jun,et al. Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification (vol 55, pg 1544, 2017)[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56(5), 3002-3003.
APA	Ye, Minchao., Qian, Yuntao., Zhou, Jun., & Tang, Yuan Yan (2018). Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification (vol 55, pg 1544, 2017). IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 56(5), 3002-3003.
MLA	Ye, Minchao,et al."Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification (vol 55, pg 1544, 2017)".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.5(2018):3002-3003.

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Affiliation No.
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh