Circle-Net: An Unsupervised Lightweight-Attention Cyclic Network for Hyperspectral and Multispectral Image Fusion
文献类型：期刊论文
| 作者 | Liu, Shuaiqi2,4; Miao, Siyu1; Liu, Siyuan1 ; Li, Bing2; Hu, Weiming2; Zhang, Yu-Dong3
|
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 16页码:4499-4515 |
| ISSN号 | 1939-1404 |
| 关键词 | Attention mechanism deep learning (DL) hyperspectral images (HSIs) image fusion multispectral images (MSIs) |
| DOI | 10.1109/JSTARS.2023.3271359 |
| 通讯作者 | Miao, Siyu(siyumiao_hbu@163.com) ; Liu, Siyuan(syliu_hbu@163.com) |
| 英文摘要 | 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. |
| WOS关键词 | RECONSTRUCTION ; FACTORIZATION |
| 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] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001010424300008 |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53492]  |
| 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, Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Leicester, Sch Comp & Math, Leicester LE1 7RH, England 4.Hebei Univ, Coll Elect & Informat Engn, Machine Vis Engn Res Ctr Hebei Prov, Baoding 071002, Peoples R China |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。