Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager

doi:10.1109/TGRS.2022.3196177

IOCAS-IR > 海洋环流与波动重点实验室

	Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager
	Zhou, Yuan 1; Chen, Keran 1; Li, Xiaofeng 2
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:17
通讯作者	Li, Xiaofeng(xiaofeng.li@ieee.org)
摘要	Sea fog significantly threatens the safety of maritime activities. This article develops a sea fog detection dataset (SFDD) and a dual-branch sea fog detection network (DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea and the Bohai Sea (118.1 degrees E-128.1 degrees E, 29.5 degrees N-43.8 degrees N) from 2010 to 2020 and collect the sea fog images for each event from the Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The location of the sea fog in each image in SFDD is accurately marked. The proposed dataset is characterized by a long-time span, a large number of samples, and accurate labeling, which can substantially improve the robustness of various sea fog detection models. Furthermore, this article proposes a DB-SFNet to achieve accurate and holistic sea fog detection. The proposed DB-SFNet is composed of a knowledge extraction module and a dual-branch optional encoding decoding module. The two modules jointly extract discriminative features from both visual and statistical domains. Experiments show promising sea fog detection results with an F1-score of 0.77 and a critical success index of 0.63. Compared with existing advanced deep learning networks, DB-SFNet is superior in detection performance and stability, particularly in the mixed cloud and fog areas.
关键词	Satellites Oceans Remote sensing Feature extraction Deep learning Color Annotations Deep learning satellite imagery sea fog detection semantic segmentation
DOI	10.1109/TGRS.2022.3196177
收录类别	SCI
语种	英语
资助项目	Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)[2022QNLM050301-2] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42000000] ; National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[62171320] ; National Natural Science Foundation of China[42090044] ; Chinese Academy of Science Program[Y9KY04101L]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000843314100012
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[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/337002/179923
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Megasci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
通讯作者单位	中国科学院海洋大科学研究中心
推荐引用方式 GB/T 7714	Zhou, Yuan,Chen, Keran,Li, Xiaofeng. Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:17.
APA	Zhou, Yuan,Chen, Keran,&Li, Xiaofeng.(2022).Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,17.
MLA	Zhou, Yuan,et al."Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):17.