DeepBlue: Advanced convolutional neural network applications for ocean remote sensing
文献类型：期刊论文
| 作者 | Wang, Haoyu2; Li, Xiaofeng1,2 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
 |
| 出版日期 | 2023-12-28 |
| 页码 | 24 |
| ISSN号 | 2473-2397 |
| DOI | 10.1109/MGRS.2023.3343623 |
| 通讯作者 | Wang, Haoyu(wanghy@qdio.ac.cn) |
| 英文摘要 | In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. Artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions. |
| WOS关键词 | GLOBAL OCEAN ; CLASSIFICATION ; TEMPERATURE ; SATELLITE ; FRAMEWORK ; MOTION ; MODEL |
| 资助项目 | National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[42221005] ; National Natural Science Foundation of China[42090044] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] |
| WOS研究方向 | Geochemistry & Geophysics ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001134395700001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/184248]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Wang, Haoyu |
| 作者单位 | 1.NOAA, Washington, DC USA 2.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Haoyu,Li, Xiaofeng. DeepBlue: Advanced convolutional neural network applications for ocean remote sensing[J]. IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE,2023:24. |
| APA | Wang, Haoyu,&Li, Xiaofeng.(2023).DeepBlue: Advanced convolutional neural network applications for ocean remote sensing.IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE,24. |
| MLA | Wang, Haoyu,et al."DeepBlue: Advanced convolutional neural network applications for ocean remote sensing".IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2023):24. |
入库方式： OAI收割
来源：海洋研究所
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