Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction
文献类型：期刊论文
| 作者 | Ping, Bo1; Su, Fenzhen2 ; Han, Xingxing1; Meng, Yunshan3
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2021 |
| 卷号 | 14页码:887-896 |
| 关键词 | Advanced microwave scanning radiometer 2 (AMSR2) deep learning moderate-resolution imaging spectroradiometer (MODIS) sea surface temperature super-resolution |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2020.3042242 |
| 通讯作者 | Ping, Bo(pingbo@tju.edu.cn) |
| 英文摘要 | Deep learning-based super-resolution (SR) methods have been widely used in natural images; however, their applications in satellite-derived sea surface temperature (SST) have not yet been fully discussed. Hence, it is necessary to analyze the validity of deep learning-based SR methods in SST reconstruction. In this study, an SR model, including multiscale feature extraction and multireceptive field mapping, was first proposed. Then, the proposed model and four other existing SR models were applied to SST reconstruction and analyzed. First, compared with the bicubic interpolation method, the SR models can improve the reconstruction accuracy. Compared with four other SR models, the proposed model can achieve the lowest mean squared error (MAE) in the East China Sea (ECS), in the northwest Pacific (NWP) and in the west Atlantic (WA), the second-lowest MAE in the southeast Pacific (SEP); the lowest root mean squared error (RMSE) in ECS andWA, the second-lowest RMSE in NWP and SEP. Additionally, ODRE model can acquire the highest or the second-highest peak single-to-noise ratio and structural similarity index in ECS, NWP, and SEP. Moreover, the number of missing pixels and SST variety are two essential factors in the SRperformance. The proposed multiscale feature extraction process can enhance the SR performance, especially for small regions and stable SST regions. Finally, while a deeper network can be helpful in achieving SR performance, the approach of simply adding more dilation convolutions may not enhance the reconstruction accuracy. |
| 资助项目 | Natural Science Foundation of Tianjin[18JCQNJC08900] ; State Key Laboratory of Resources and Environmental Information System |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000640582500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Natural Science Foundation of Tianjin ; State Key Laboratory of Resources and Environmental Information System |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/161680]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ping, Bo |
| 作者单位 | 1.Tianjin Univ, Inst Surface Earth Syst Sci, Sch Earth Syst Sci, Tianjin 300072, Peoples R China 2.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, Beijing 100101, Peoples R China 3.Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ping, Bo,Su, Fenzhen,Han, Xingxing,et al. Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:887-896. |
| APA | Ping, Bo,Su, Fenzhen,Han, Xingxing,&Meng, Yunshan.(2021).Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,887-896. |
| MLA | Ping, Bo,et al."Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):887-896. |
入库方式： OAI收割
来源：地理科学与资源研究所
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