基于迁移学习的太空台风自动识别

夏凯; 邢赞扬; 张清和; 王艳玲; 杨秋菊; 陆盛; 刘振平

doi:10.11728/cjss2023.02.2022-0031

基于迁移学习的太空台风自动识别

doi: 10.11728/cjss2023.02.2022-0031

夏凯^1,,
邢赞扬^2, ,,
张清和²,
王艳玲^1, ,,
杨秋菊³,
陆盛²,
刘振平¹

1.
山东大学机电与信息工程学院　威海　264209
2.
山东大学空间科学研究院山东省光学天文与日地太空环境重点实验室　威海　264209
3.
陕西师范大学物理学与信息技术学院　西安　710062

基金项目: 国家自然科学基金项目资助（42120104003，41904169，41874170）

详细信息

作者简介:
夏凯：E-mail：xiakai@mail.sdu.edu.cn

通讯作者:
邢赞扬，E-mail：xingzanyang@sdu.edu.cn

王艳玲，E-mail：wangyanling@sdu.edu.cn

中图分类号: P352
计量
- 文章访问数: 128
- HTML全文浏览量: 17
- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2022-07-11
- 录用日期: 2022-11-21
- 修回日期: 2022-10-22
- 网络出版日期: 2022-11-28

Automatic Identification of Space Hurricane Based on Transfer Learning

1.
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209
2.
Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong University, Weihai 264209
3.
School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062

摘要

摘要: 太空台风是极盖区内一种新发现的大尺度亮斑状极光结构，直观表征了地磁平静期的一种堪比磁暴的太阳风能量注入现象，这更新了人们对太阳风–磁层–电离层耦合过程的认识，如何从海量星载极光数据中准确髙效识别出太空台风事件具有重要的科学意义。采用深度学习的方法，通过六种网络模型的对比，最终基于迁移学习和EfficientNetB2网络提出了一种太空台风自动识别方法。在2005－2021年美国国防气象卫星（Defense Meteorological Satellite Program，DMSP）上搭载的紫外光谱成像仪（Special Sensor Ultraviolet Spectrographic Imager，SSUSI）的观测数据中验证了该模型的有效性，识别准确率达到97.7%。研究结果表明，该方法可用于从海量星载极光观测数据中自动识别太空台风事件。
- 太空台风 /
- 极光 /
- 迁移学习 /
- EfficientNetB2
Abstract: Space hurricane is a newly discovered large-scale and bright spot-like auroral structure in the polar cap region, which visually characterizes a solar wind energy injection phenomenon comparable to a magnetic storm during the geomagnetic calm period, this updates the understanding of the solar wind-magnetosphere-ionosphere coupling process, it is of great scientific importance to accurately and efficiently identify space hurricane events from the huge amount of auroral data. Deep learning is used, six networks are compared, and an automatic space hurricane identification method based on Transfer learning and EfficientNetB2 is proposed, validate the effectiveness of the model in DMSP/SSUSI observations from 2005 to 2021 with an accuracy of 97.7%. The results show that the method can be used to automatically identify Space hurricane events from a large amount of satellite-based auroral observation data.
- Space hurricane /
- Aurora /
- Transfer learning /
- EfficientNetB2
注释:

1) *https://ssusi.jhuapl.edu/gal_Aur

HTML全文

图 1 太空台风自动识别算法构架

Figure 1. Architecture diagram of space hurricane automatic identification

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图 2 迁移学习策略

Figure 2. Transfer learning strategy

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图 3 太空台风事例

Figure 3. Examples of space hurricane

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图 4 数据预处理

Figure 4. Data pre-processing

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图 5 6种模型训练集损失率(a)与验证集准确率(b)的变化情况

Figure 5. Variation of loss rate in the training set (a) and accuracy rate in the validation set (b) of six models

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图 6 6种模型测试集的混淆矩阵

Figure 6. Confusion matrices of six models in the test set

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图 7 MBConv结构

Figure 7. MBConv structure

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图 8 太空台风识别卷积层的32个特征图

Figure 8. 32 feature maps of space hurricane recognition convolution layer

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图 9 太空台风误判事件

Figure 9. Misjudgements of space hurricane

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表 1 混淆矩阵

Table 1. Confusion matrix

Confusion matrix		True labels
Confusion matrix		1	0
Predicted labels	1	n₁	n₂
	0	n₃	n₄

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表 2 6种模型测试集的评价指标

Table 2. Evaluation metrics of six models in the test set

Model name	a	p	r	s	F₁
VGG	0.9700	0.9930	0.9467	0.9933	0.9693
ResNet	0.9667	0.9795	0.9533	0.9800	0.9662
EfficientNetB0	0.9767	0.9799	0.9733	0.9800	0.9766
EfficientNetB2	0.9767	0.9735	0.9800	0.9733	0.9767
EfficientNetV2-S	0.9200	0.9200	0.9200	0.9200	0.9200
EfficientNetV2-M	0.9400	0.9459	0.9333	0.9467	0.9396

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表 3 EfficientNetB0的体系结构

Table 3. Architecture of EfficientNetB0

Stage	Operator	Resolution	Channels	Layers
1	Conv3×3	224×224	32	1
2	MBConv1, k3×3	112×112	16	1
3	MBConv6, k3×3	112×112	24	2
4	MBConv6, k5×5	56×56	40	2
5	MBConv6, k3×3	28×28	80	3
6	MBConv6, k5×5	14×14	112	3
7	MBConv6, k5×5	14×14	192	4
8	MBConv6, k3×3	7×7	320	1
9	Conv1×1&Pooling&FC	7×7	1280	1

下载: 导出CSV

参考文献(21)

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