| 基于改进U-Net网络的海洋中尺度涡自动检测模型 |
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| 引用本文： | 董子意,杜震洪,吴森森,李亚东,张丰,刘仁义.基于改进U-Net网络的海洋中尺度涡自动检测模型[J].海洋学报,2022,44(2):123-131. |
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| 作者姓名： | 董子意 杜震洪 吴森森 李亚东 张丰 刘仁义 |
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| 作者单位： | 1.浙江大学 地球科学学院，浙江 杭州 310027 |
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| 基金项目： | 国家自然科学基金(41922043,41871287,42001323)。 |
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| 摘 要： | 海洋中尺度涡对浮游生物的分布、能量和盐分的输送具有非常重要的影响，海洋中尺度涡的自动检测是监测、分析中尺度涡时空变化的重要基础。针对传统基于物理特征检测海洋中尺度涡的方法存在受限于人工设计参数导致精度不高的问题，本文依据海洋卫星反演的海表面高度图，提出了一种基于改进U-Net网络的海洋中尺度涡自动检测模型。该模型在海洋中尺度涡的特征提取阶段嵌入了卷积注意力机制，使得模型能够关注于海表面高度图中最具有类别区分度的区域，同时引入了残差学习机制解决了网络过深导致模型难以训练的问题。本文以南大西洋的卫星海表面高度数据集为例开展实验验证，结果表明，本文提出的模型海洋中尺度涡检测准确率达到了93.28%，显著优于EddyNet等现有模型。模型可为海洋学家通过海表面高度探测中尺度涡提供可靠技术方法。
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| 关 键 词： | 海洋中尺度涡 深度学习 语义分割 注意力机制 残差学习 |
| 收稿时间： | 2021-01-09 |
An automatic marine mesoscale eddy detection model based on improved U-Net network |
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| Dong Ziyi,Du Zhenhong,Wu Sensen,Li Yadong,Zhang Feng,Liu Renyi.An automatic marine mesoscale eddy detection model based on improved U-Net network[J].Acta Oceanologica Sinica (in Chinese),2022,44(2):123-131. |
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| Authors: | Dong Ziyi Du Zhenhong Wu Sensen Li Yadong Zhang Feng Liu Renyi |
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| Institution: | 1.School of Earth Sciences, Zhejiang University, Hangzhou 310027, China2.Zhejiang Provincial Key Laboratory of Geograpic Information System, Hangzhou 310028, China3.Department of Geographic and Spatial Information Science, Zhejiang University, Hangzhou 310027, China |
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| Abstract: | Marine mesoscale eddies play an important role in plankton distribution, energy and salt transport. The automatic detection of marine mesoscale eddies is a basis for monitoring and analyzing their spatiotemporal variations. Traditional physical characteristics-based methods depend on artificially designing parameters, and result in low accuracy of mesoscale eddy extraction. Therefore, a marine mesoscale eddy automatic detection model based on improved U-Net network according to the marine satellite sea surface height images is proposed in this paper. The proposed model embeds the convolution attention modules in the feature extraction stage, which enables the model focuses on the most relevent area. Meanwhile, the model introduces the residual learning module to solve the problem that the network is too deep to train the model. In this paper, the satellite sea surface height dataset in the South Atlantic Ocean is taken as an example to carry out experiments. The results show that the proposed model achieves a high accuracy of 93.28% when detecting the marine mesoscale eddies, which is significantly better than EddyNet and other models. The model can provide a reliable technology for oceanographers to detect marine mesoscale eddies through the satellite sea surface height. |
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| Keywords: | marine mesoscale eddy deep learning semantic segmentation convolutional block attention module residual learning |
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