Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager
文献类型：期刊论文
| 作者 | Zhou, Yuan1; Chen, Keran1; Li, Xiaofeng2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:17 |
| 关键词 | Satellites Oceans Remote sensing Feature extraction Deep learning Color Annotations Deep learning satellite imagery sea fog detection semantic segmentation |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2022.3196177 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000843314100012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/179922]  |
| 专题 | 海洋研究所_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 |
| 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. |
入库方式： OAI收割
来源：海洋研究所
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