CASIA OpenIR
Circle-Net: An Unsupervised Lightweight-Attention Cyclic Network for Hyperspectral and Multispectral Image Fusion
Liu, Shuaiqi1,2; Miao, Siyu3; Liu, Siyuan3; Li, Bing2; Hu, Weiming2; Zhang, Yu-Dong4
发表期刊IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
2023
卷号16页码:4499-4515
通讯作者Miao, Siyu(siyumiao_hbu@163.com) ; Liu, Siyuan(syliu_hbu@163.com)
摘要Hyperspectral image (HSI) and multispectral image (MSI) fusion has the potential to significantly improve the quality and usefulness of data, leading to better decision-making and a more complete understanding of the observed scene. For HSI and MSI fusion, capturing matched pairs of HSI and MSI images is challenging. This hampers the pretraining of neural-network-based HSI-MSI fusion methods and yields unsatisfactory fusion results. A lightweight-attention (LA) cyclic network (Circle-Net) without pretraining using labeled data is constructed and applied to HSI-MSI fusion to alleviate this issue. Circle-Net consists of a coordinate feature fusion (CFF) network and a dual-attention decoder (DAD) network. Multiscale features collected from the DAD network are fused by the CFF network to derive a high-resolution HSI. Specifically, in the DAD network, skip connections in the encoder-decoder network are replaced by LAs, while polarized attention is used to guarantee efficient transfer of features between the encoder and decoder. In comparison with other methods, the experimental performance shows the superiority of the Circle-Net in both visual and quantitative performance.
关键词Attention mechanism deep learning (DL) hyperspectral images (HSIs) image fusion multispectral images (MSIs)
DOI10.1109/JSTARS.2023.3271359
关键词[WOS]RECONSTRUCTION ; FACTORIZATION
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[62172139] ; National Natural Science Foundation of China[U1936204] ; National Key RD Plan[2020AAA0106800] ; Natural Science Foundation of Hebei Province[F2022201055] ; China Postdoctoral[2022M713361] ; Science Research Project of Hebei Province[BJ2020030] ; Natural Science Interdisciplinary Research Program of Hebei University[DXK202102] ; Open Project Program of NLPR[202200007]
项目资助者National Natural Science Foundation of China ; National Key RD Plan ; Natural Science Foundation of Hebei Province ; China Postdoctoral ; Science Research Project of Hebei Province ; Natural Science Interdisciplinary Research Program of Hebei University ; Open Project Program of NLPR
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:001010424300008
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
被引频次:4[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/173211/53493
专题中国科学院自动化研究所
通讯作者Miao, Siyu; Liu, Siyuan
作者单位1.Hebei Univ, Coll Elect & Informat Engn, Machine Vis Engn Res Ctr Hebei Prov, 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.Univ Leicester, Sch Comp & Math, Leicester LE1 7RH, England
第一作者单位模式识别国家重点实验室
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Liu, Shuaiqi,Miao, Siyu,Liu, Siyuan,et al. Circle-Net: An Unsupervised Lightweight-Attention Cyclic Network for Hyperspectral and Multispectral Image Fusion[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2023,16:4499-4515.
APA Liu, Shuaiqi,Miao, Siyu,Liu, Siyuan,Li, Bing,Hu, Weiming,&Zhang, Yu-Dong.(2023).Circle-Net: An Unsupervised Lightweight-Attention Cyclic Network for Hyperspectral and Multispectral Image Fusion.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,16,4499-4515.
MLA Liu, Shuaiqi,et al."Circle-Net: An Unsupervised Lightweight-Attention Cyclic Network for Hyperspectral and Multispectral Image Fusion".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16(2023):4499-4515.
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