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UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion

文献类型：期刊论文

作者Liu, Shuaiqi1,2; Miao, Siyu3; Su, Jian4; Li, Bing2; Hu, Weiming2; Zhang, Yu-Dong5
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2021
卷号14页码:7373-7385
关键词Tensors Image fusion Hyperspectral imaging Spatial resolution Feature extraction Image reconstruction Dictionaries Deep learning hyperspectral images (HSIs) image fusion multispectral images (MSIs)
ISSN号1939-1404
DOI10.1109/JSTARS.2021.3097178
通讯作者Su, Jian(sj890718@gmail.com) ; Zhang, Yu-Dong(yudongzhang@ieee.org)
英文摘要To reconstruct images with high spatial resolution and high spectral resolution, one of the most common methods is to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral image (MSI) of the same scene. Deep learning has been widely applied in the field of HSI-MSI fusion, which is limited with hardware. In order to break the limits, we construct an unsupervised multiattention-guided network named UMAG-Net without training data to better accomplish HSI-MSI fusion. UMAG-Net first extracts deep multiscale features of MSI by using a multiattention encoding network. Then, a loss function containing a pair of HSI and MSI is used to iteratively update parameters of UMAG-Net and learn prior knowledge of the fused image. Finally, a multiscale feature-guided network is constructed to generate an HR-HSI. The experimental results show the visual and quantitative superiority of the proposed method compared to other methods.
WOS关键词MANIFOLD ALIGNMENT ; MULTIBAND IMAGES ; FRAMEWORK ; CLASSIFICATION ; FACTORIZATION ; REGRESSION
资助项目Natural Science Foundation of Hebei Province[F2020201025] ; Natural Science Foundation of Hebei Province[F2019201151] ; Natural Science Foundation of Hebei Province[F2018210148] ; Science Research Project of Hebei Province[BJ2020030] ; Science Research Project of Hebei Province[QN2017306] ; National Natural Science Foundation of China[61572063] ; National Natural Science Foundation of China[62172003]
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000682121200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Natural Science Foundation of Hebei Province ; Science Research Project of Hebei Province ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/45672]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Su, Jian; Zhang, Yu-Dong
作者单位1.Hebei Univ, Machine Vis Technol Innovat Ctr Hebei, Coll Elect & Informat Engn, Baoding 071002, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China
4.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210094, Peoples R China
5.Univ Leicester, Dept Informat, Leicester LE1 7RH, Leics, England
推荐引用方式
GB/T 7714		 Liu, Shuaiqi,Miao, Siyu,Su, Jian,et al. UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:7373-7385.
APA Liu, Shuaiqi,Miao, Siyu,Su, Jian,Li, Bing,Hu, Weiming,&Zhang, Yu-Dong.(2021).UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,7373-7385.
MLA Liu, Shuaiqi,et al."UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):7373-7385.

入库方式： OAI收割

来源：自动化研究所

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