IOCAS-IR  > 海洋环流与波动重点实验室
DeepBlue: Advanced convolutional neural network applications for ocean remote sensing
Wang, Haoyu1; Li, Xiaofeng1,2
2023-12-28
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
ISSN2473-2397
Pages24
Corresponding AuthorWang, Haoyu(wanghy@qdio.ac.cn)
AbstractIn 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.
DOI10.1109/MGRS.2023.3343623
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaGeochemistry & Geophysics ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:001134395700001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS KeywordGLOBAL OCEAN ; CLASSIFICATION ; TEMPERATURE ; SATELLITE ; FRAMEWORK ; MOTION ; MODEL
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/184249
Collection海洋环流与波动重点实验室
Corresponding AuthorWang, Haoyu
Affiliation1.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.NOAA, Washington, DC USA
First Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Recommended Citation
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.
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