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

doi:10.1109/JSTARS.2021.3097178

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion
	Liu, Shuaiqi 1,2; Miao, Siyu 3; Su, Jian 4; Li, Bing2 ; Hu, Weiming2 ; Zhang, Yu-Dong 5
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
	2021
卷号	14 页码:7373-7385
通讯作者	Su, Jian(sj890718@gmail.com) ; Zhang, Yu-Dong(yudongzhang@ieee.org)
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.
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)
DOI	10.1109/JSTARS.2021.3097178
关键词[WOS]	MANIFOLD ALIGNMENT ; MULTIBAND IMAGES ; FRAMEWORK ; CLASSIFICATION ; FACTORIZATION ; REGRESSION
收录类别	SCI
语种	英语
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]
项目资助者	Natural Science Foundation of Hebei Province ; Science Research Project of Hebei Province ; National Natural Science Foundation of China
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000682121200001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：23[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45673
专题	多模态人工智能系统全国重点实验室_视频内容安全
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
第一作者单位	模式识别国家重点实验室
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.