DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

doi:10.1109/MGRS.2023.3343623

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, Haoyu 1; Li, Xiaofeng 1,2
	2023-12-28
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
ISSN	2473-2397
页码	24
通讯作者	Wang, Haoyu(wanghy@qdio.ac.cn)
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.
DOI	10.1109/MGRS.2023.3343623
收录类别	SCI
语种	英语
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
WOS类目	Geochemistry & Geophysics ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001134395700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	GLOBAL OCEAN ; CLASSIFICATION ; TEMPERATURE ; SATELLITE ; FRAMEWORK ; MOTION ; MODEL
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/184249
专题	海洋环流与波动重点实验室
通讯作者	Wang, Haoyu
作者单位	1.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.NOAA, Washington, DC USA
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
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.

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文件名称/大小	文献类型	版本类型	开放类型	使用许可
DeepBlue_Advanced_co（4818KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	浏览