Hyperspectral Image Superresolution by Transfer Learning

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Hyperspectral Image Superresolution by Transfer Learning
	Yuan, Yuan; Zheng, Xiangtao; Lu, Xiaoqiang
刊名	ieee journal of selected topics in applied earth observations and remote sensing
	2017-05-01
卷号	10 期号:5 页码:1963-1974
关键词	Collaborative nonnegative matrix factorization (CNMF) convolutional neural network (CNN) hyperspectral image (HSI) superresolution
ISSN号	1939-1404
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.
产权排序	1
英文摘要	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.
WOS标题词	science & technology ; technology ; physical sciences
类目[WOS]	engineering, electrical & electronic ; geography, physical ; remote sensing ; imaging science & photographic technology
研究领域[WOS]	engineering ; physical geography ; remote sensing ; imaging science & photographic technology
关键词[WOS]	sparse regression ; regularization ; subspace ; saliency ; fusion
收录类别	SCI
语种	英语
WOS记录号	WOS:000399682500024
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/28874]
专题	西安光学精密机械研究所_光学影像学习与分析中心
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. 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.(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.