太空台风的多波段图像联合目标检测方法研究

doi:10.11871/jfdc.issn.2096-742X.2025.04.010

数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (4): 118-128.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.04.010

doi: 10.11871/jfdc.issn.2096-742X.2025.04.010

• 专刊：空间科学大数据智能算法模型与工具 • 上一篇下一篇

太空台风的多波段图像联合目标检测方法研究

石珂¹(),陆阳^2,³,陆盛⁴,王勇⁴,邹自明^2,^3,^*()

1.中国科学院大学，北京 100049
2.中国科学院国家空间科学中心，北京 100190
3.国家空间科学数据中心，北京 100190
4.山东大学威海分校，山东威海 264209

收稿日期:2025-04-30 出版日期:2025-08-20 发布日期:2025-08-21
通讯作者: 邹自明
作者简介:石珂，中国科学院国家空间科学中心，硕士研究生，主要研究方向为空间大数据处理与应用。
本文承担工作为实验设计、数据处理、模型实现。
SHI Ke, is a master's student from the National Space Science Center, Chinese Academy of Sciences. Her main research interests include the processing and application of space big data.
In this paper, she is mainly responsible for experimental design, data processing, and model implementation.
E-mail: shike231@mails.ucas.ac.cn|邹自明，中国科学院国家空间科学中心，博士生导师，主要研究方向为日地空间大数据处理与应用技术、空间大数据处理与应用。
本文承担工作为实验与论文指导。
ZOU Ziming, is a doctoral supervisor at the National Space Science Center, Chinese Academy of Sciences. His main research interests include the processing and application technologies of solar-terrestrial space big data, as well as the processing and application of space big data.
In this paper, he is mainly responsible for providing guidance for the experiment and the thesis.
E-mail: mzou@nssc.ac.cn
基金资助:
国家重点研发计划“基础科研条件与重大科学仪器设备研发”重点专项(2022YFF0711400);中国科学院“十四五”网络安全和信息化专项(CAS-WX2022SDC-XK15);中国科学院网信专项(CAS-WX2022SF-0103)

Research on Joint Object Detection Method for Multi-Band Images of Space Hurricane

SHI Ke¹(),LU Yang^2,³,LU Sheng⁴,WANG Yong⁴,ZOU Ziming^2,^3,^*()

1. University of Chinese Academy of Sciences, Beijing 100049, China
2. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
3. National Space Science Data Center, Beijing 100190, China
4. Shandong University at Weihai, Weihai, Shandong 264209, China

Received:2025-04-30 Online:2025-08-20 Published:2025-08-21
Contact: ZOU Ziming

摘要/Abstract

摘要：

【目的】太空台风作为日地相互作用引发的典型现象，其在中高层大气中巨大的能量气旋往往伴随着极光现象的产生。通过对极光图像的识别可以帮助科学家寻找典型的太空台风事件，然而目前寻找事件主要依赖专家对极光图像的人工鉴别，较为低效。为解决上述问题，本文探索了基于深度学习的多波段图像联合目标检测方法，实现了对太空台风的事件识别与精准定位。【方法】本文利用DMSP/SSUSI的121.6 nm、135.6 nm、LBHS、LBHL四个波段极光图像识别太空台风事件，基于YOLOv8算法框架，引入了目标级融合与特征级融合策略，同时建立了单波段以及多波段融合的太空台风识别模型。【结果】在事件识别任务中，通过单波段基准模型与多波段融合模型的实验结果进行对比。结果显示特征级融合中的1216_LBHL组合表现最优，F1值达0.941；目标检测任务中，目标级融合中的1216_LBHL组合AP值最高，为0.917。【结论】特征级融合在太空台风事件识别中更具优势，目标级融合则更适用于目标检测任务，说明多波段互补性与融合策略的组合优化是提升检测性能的关键。

关键词: 太空台风, YOLOv8, 目标级融合, 特征级融合

Abstract:

[Objective] As a typical phenomenon triggered by solar-terrestrial interactions, space hurricane often generates a huge energy cyclone in the middle and upper atmosphere, which is accompanied by the occurrence of auroral phenomena. Identifying auroral images can assist scientists in finding typical space hurricane events. However, currently, the search for such events mainly relies on experts' manual identification of auroral images, which is rather inefficient. To solve the above problems, this study explores a deep learning-based joint object detection method for multi-band images, achieving event recognition and precise localization of space hurricanes. [Methods] In this study, four-band auroral images (121.6nm, 135.6nm, LBHS, LBHL) from DMSP/SSUSI are used to identify space hurricane events. Based on the YOLOv8 algorithm framework, target-level fusion and feature-level fusion strategies are introduced. Meanwhile, single-band and multi-band fusion models for space hurricane recognition are established. [Results] In the event recognition task, by comparing the experimental results of single-band baseline models and multi-band fusion models, it is shown that the 1216_LBHL combination in feature-level fusion performs best, with an F1 score of 0.941. In the object detection task, the 1216_LBHL combination in target-level fusion achieves the highest AP value of 0.917. [Conclusions] Feature-level fusion demonstrates greater advantages in space hurricane event recognition, while target-level fusion is more suitable for object detection tasks. This indicates that the combined optimization of multi-band complementarity and fusion strategies is the key to enhancing detection performance.

Key words: space hurricane, YOLOv8, target-level fusion, feature-level fusion

石珂, 陆阳, 陆盛, 王勇, 邹自明. 太空台风的多波段图像联合目标检测方法研究[J]. 数据与计算发展前沿, 2025, 7(4): 118-128.

SHI Ke, LU Yang, LU Sheng, WANG Yong, ZOU Ziming. Research on Joint Object Detection Method for Multi-Band Images of Space Hurricane[J]. Frontiers of Data and Computing, 2025, 7(4): 118-128, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2025.04.010.

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波段	Precision	Recall	F1值
1216	1.000	0.056	0.105
1356	0.857	0.833	0.845
LBHS	0.882	0.833	0.857
LBHL	0.903	0.778	0.836

波段组合	融合方法	Precision	Recall	F1值
1216_1356	目标级融合	1.000	0.086	0.158
1216_LBHS	目标级融合	0.861	0.886	0.873
1216_LBHL	目标级融合	0.868	0.943	0.904
1356_LBHS	目标级融合	0.775	0.886	0.827
1356_LBHL	目标级融合	0.825	0.943	0.880
LBHS_LBHL	目标级融合	0.906	0.829	0.866
1216_1356_LBHS	目标级融合	1.000	0.829	0.906
1216_1356_LBHL	目标级融合	1.000	0.800	0.889
1216_LBHS_LBHL	目标级融合	0.865	0.914	0.889
1356_LBHS_LBHL	目标级融合	0.806	0.829	0.817
1216_1356_LBHS_LBHL	目标级融合	0.941	0.914	0.928
1216_1356	特征级融合	1.000	0.829	0.906
1216_LBHS	特征级融合	0.872	0.971	0.919
1216_LBHL	特征级融合	0.970	0.914	0.941
1356_LBHS	特征级融合	0.968	0.857	0.909
1356_LBHL	特征级融合	0.846	0.943	0.892
LBHS_LBHL	特征级融合	0.857	0.857	0.857
1216_1356_LBHS	特征级融合	0.906	0.829	0.866
1216_1356_LBHL	特征级融合	0.912	0.886	0.899
1216_LBHS_LBHL	特征级融合	0.912	0.886	0.899
1356_LBHS_LBHL	特征级融合	0.939	0.886	0.912
1216_1356_LBHS_LBHL	特征级融合	0.886	0.886	0.886

波段	Precision	Recall	AP
1216	1.000	0.056	0.528
1356	0.800	0.778	0.828
LBHS	0.808	0.778	0.823
LBHL	0.839	0.722	0.798

波段组合	融合方法	Precision	Recall	AP
1216_1356	目标级融合	1.000	0.086	0.543
1216_LBHS	目标级融合	0.806	0.829	0.831
1216_LBHL	目标级融合	0.846	0.886	0.917
1356_LBHS	目标级融合	0.809	0.743	0.792
1356_LBHL	目标级融合	0.732	0.857	0.824
LBHS_LBHL	目标级融合	0.844	0.771	0.841
1216_1356_LBHS	目标级融合	0.931	0.771	0.849
1216_1356_LBHL	目标级融合	0.964	0.771	0.872
1216_LBHS_LBHL	目标级融合	0.837	0.800	0.828
1356_LBHS_LBHL	目标级融合	0.806	0.829	0.870
1216_1356_LBHS_LBHL	目标级融合	0.968	0.800	0.881
1216_1356	特征级融合	0.862	0.714	0.792
1216_LBHS	特征级融合	0.822	0.857	0.854
1216_LBHL	特征级融合	0.848	0.800	0.809
1356_LBHS	特征级融合	0.903	0.800	0.868
1356_LBHL	特征级融合	0.792	0.800	0.804
LBHS_LBHL	特征级融合	0.912	0.714	0.829
1216_1356_LBHS	特征级融合	0.909	0.771	0.868
1216_1356_LBHL	特征级融合	0.878	0.826	0.871
1216_LBHS_LBHL	特征级融合	0.878	0.826	0.871
1356_LBHS_LBHL	特征级融合	0.857	0.857	0.852
1216_1356_LBHS_LBHL	特征级融合	0.829	0.829	0.873

太空台风的多波段图像联合目标检测方法研究

Research on Joint Object Detection Method for Multi-Band Images of Space Hurricane

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