Scholarlink

<p>Monitoring solar phenomena, such as sunspots and active regions, is crucial for ensuring astronaut safety, telecommunications reliability, and predicting terrestrial events like auroras. Traditional methods for detecting these phenomena have limitations in accuracy and baseline maintenance. This paper presents a novel deep learning object detection method that leverages multispectral image data from satellites to enhance the detection of "sunspots" and active regions. Utilizing images from the SDO satellite and annotations from the DeepSDO dataset, we constructed a new dataset composed of aligned observations from HMI Ic, AIA 211&#xa0;Å, and AIA 335&#xa0;Å. We adapted and developed a stock YOLOv5-based model capable of handling and fusing any number of input images. Two fusion methodologies, early and late fusion, and three different fusion modules—CatFuse (simple concatenation), CBAMC (CBAM-based module), and TransEnc (transformer encoder)—were implemented and tested. Statistically analysing the results via the Friedman test (<i>p</i>=0.05) revealed significant performance differences among the evaluated models, which were confirmed through pairwise Wilcoxon post-hoc tests. From the approaches tested, CatFuse with early fusion achieved significantly higher detection performance than the other models, with a mAP@0.5:0.95 of 0.52 and a mAP@0.5 of 0.94, an improvement of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Delta \)</EquationSource> </InlineEquation>=0.02–0.36 for mAP@0.5:0.95 and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\Delta \)</EquationSource> </InlineEquation>=0.02–0.28 for mAP@0.5, depending on the baseline model. This result was marginally better than the best baseline (YOLOv5 with a single HMI image) and comparable to other state-of-the-art models, demonstrating a modest but consistent improvement of multispectral image fusion for this task.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multispectral YOLO: generic feature fusion framework for solar active region detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

查看全文
  • António Santos,
  • Filipa S. Barros,
  • J. J. G. Lima,
  • Rui F. Pinto,
  • André Restivo,
  • Luís F. Teixeira

摘要

Monitoring solar phenomena, such as sunspots and active regions, is crucial for ensuring astronaut safety, telecommunications reliability, and predicting terrestrial events like auroras. Traditional methods for detecting these phenomena have limitations in accuracy and baseline maintenance. This paper presents a novel deep learning object detection method that leverages multispectral image data from satellites to enhance the detection of "sunspots" and active regions. Utilizing images from the SDO satellite and annotations from the DeepSDO dataset, we constructed a new dataset composed of aligned observations from HMI Ic, AIA 211 Å, and AIA 335 Å. We adapted and developed a stock YOLOv5-based model capable of handling and fusing any number of input images. Two fusion methodologies, early and late fusion, and three different fusion modules—CatFuse (simple concatenation), CBAMC (CBAM-based module), and TransEnc (transformer encoder)—were implemented and tested. Statistically analysing the results via the Friedman test (p=0.05) revealed significant performance differences among the evaluated models, which were confirmed through pairwise Wilcoxon post-hoc tests. From the approaches tested, CatFuse with early fusion achieved significantly higher detection performance than the other models, with a mAP@0.5:0.95 of 0.52 and a mAP@0.5 of 0.94, an improvement of \(\Delta \) =0.02–0.36 for mAP@0.5:0.95 and \(\Delta \) =0.02–0.28 for mAP@0.5, depending on the baseline model. This result was marginally better than the best baseline (YOLOv5 with a single HMI image) and comparable to other state-of-the-art models, demonstrating a modest but consistent improvement of multispectral image fusion for this task.