DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

doi:10.1109/MGRS.2023.3343623

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	DeepBlue: Advanced convolutional neural network applications for ocean remote sensing
	Wang, Haoyu 2; Li, Xiaofeng 1,2
刊名	IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
	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.
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关键词	GLOBAL OCEAN ; CLASSIFICATION ; TEMPERATURE ; SATELLITE ; FRAMEWORK ; MOTION ; MODEL
WOS研究方向	Geochemistry & Geophysics ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001134395700001
内容类型	期刊论文
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.