Hyperspectral Image Superresolution by Transfer Learning

doi:10.1109/JSTARS.2017.2655112

OPT OpenIR > 光谱成像技术研究室

	Hyperspectral Image Superresolution by Transfer Learning
	Yuan, Yuan; Zheng, Xiangtao; Lu, Xiaoqiang; Lu, XQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China.
作者部门	光学影像学习与分析中心
	2017-05-01
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
卷号	10 期号:5 页码:1963-1974
产权排序	1
摘要	Hyperspectral image superresolution is a highly attractive topic in computer vision and has attracted many researchers' attention. However, nearly all the existing methods assume that multiple observations of the same scene are required with the observed low-resolution hyperspectral image. This limits the application of superresolution. In this paper, we propose a new framework to enhance the resolution of hyperspectral images by exploiting the knowledge from natural images: The relationship between low/high-resolution images is the same as that between low/high-resolution hyperspectral images. In the proposed framework, the mapping between low-and high-resolution images can be learned by deep convolutional neural network and be transferred to hyperspectral image by borrowing the idea of transfer learning. In addition, to study the spectral characteristic between low-and high-resolution hyperspectral image, collaborative nonnegative matrix factorization (CNMF) is proposed to enforce collaborations between the low-and high-resolution hyperspectral images, which encourages the estimated solution to extract the same endmembers with low-resolution hyperspectral image. The experimental results on ground based and remote sensing data suggest that the proposed method achieves comparable performance without requiring any auxiliary images of the same scene.
文章类型	Article
关键词	Collaborative Nonnegative Matrix Factorization (Cnmf) Convolutional Neural Network (Cnn) Hyperspectral Image (Hsi) Superresolution
WOS标题词	Science & Technology ; Technology ; Physical Sciences
DOI	10.1109/JSTARS.2017.2655112
收录类别	SCI
关键词[WOS]	SPARSE REGRESSION ; REGULARIZATION ; SUBSPACE ; SALIENCY ; FUSION
语种	英语
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
项目资助者	National Basic Research Program of China (Youth 973 Program)(2013CB336500) ; State Key Program of National Natural Science of China(60632018 ; National Natural Science Foundation of China(61472413) ; Key Research Program of the Chinese Academy of Sciences(KGZD-EW-T03) ; Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408) ; Young Top-Notch Talent Program of Chinese Academy of Sciences(QYZDB-SSW-JSC015) ; 61232010)
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000399682500024
引用统计	被引频次：195[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/28874
专题	光谱成像技术研究室
通讯作者	Lu, XQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China.
作者单位	Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Yuan,Zheng, Xiangtao,Lu, Xiaoqiang,et al. Hyperspectral Image Superresolution by Transfer Learning[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(5):1963-1974.
APA	Yuan, Yuan,Zheng, Xiangtao,Lu, Xiaoqiang,&Lu, XQ .(2017).Hyperspectral Image Superresolution by Transfer Learning.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(5),1963-1974.
MLA	Yuan, Yuan,et al."Hyperspectral Image Superresolution by Transfer Learning".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.5(2017):1963-1974.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Hyperspectral Image （1014KB）	期刊论文	作者接受稿	限制开放	CC BY-NC-SA	请求全文