Automatic Identification and Segmentation of Diffuse Aurora from Untrimmed All-Sky Auroral Videos.
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| Title: | Automatic Identification and Segmentation of Diffuse Aurora from Untrimmed All-Sky Auroral Videos. |
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| Authors: | Wang, Qian1,2,3 (AUTHOR), Hao, Peiqi1,2 (AUTHOR), Pan, Han3,4 (AUTHOR) panhan@sjtu.edu.cn |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 3, p402. 30p. |
| Subjects: | Auroras, Video processing, Automatic identification, Space sciences, Remote sensing |
| Abstract: | Highlights: What are the main findings? An automated framework achieves 96.3% frame-wise accuracy, 87.7% Edit score, and F1@50 of 83.0% for diffuse aurora identification and temporal segmentation in long ASI videos. Large-scale statistical analysis of 358,560 all-sky images reveals a robust diurnal pattern of diffuse aurora occurrence, with a consistent peak around 07:00 UT and a minimum near 11:00 UT across multiple years. What are the implications of the main findings? The results demonstrate that physics-informed, vision-based methods can reliably extract physically meaningful auroral statistics from massive remote sensing video archives without manual inspection. The derived occurrence statistics provide quantitative observational support for established diffuse aurora particle precipitation and drift mechanisms, enabling scalable studies of magnetosphere-ionosphere coupling. Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191586540 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic Identification and Segmentation of Diffuse Aurora from Untrimmed All-Sky Auroral Videos. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Qian%22">Wang, Qian</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hao%2C+Peiqi%22">Hao, Peiqi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Han%22">Pan, Han</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> panhan@sjtu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 3, p402. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Auroras%22">Auroras</searchLink><br /><searchLink fieldCode="DE" term="%22Video+processing%22">Video processing</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+identification%22">Automatic identification</searchLink><br /><searchLink fieldCode="DE" term="%22Space+sciences%22">Space sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? An automated framework achieves 96.3% frame-wise accuracy, 87.7% Edit score, and F1@50 of 83.0% for diffuse aurora identification and temporal segmentation in long ASI videos. Large-scale statistical analysis of 358,560 all-sky images reveals a robust diurnal pattern of diffuse aurora occurrence, with a consistent peak around 07:00 UT and a minimum near 11:00 UT across multiple years. What are the implications of the main findings? The results demonstrate that physics-informed, vision-based methods can reliably extract physically meaningful auroral statistics from massive remote sensing video archives without manual inspection. The derived occurrence statistics provide quantitative observational support for established diffuse aurora particle precipitation and drift mechanisms, enabling scalable studies of magnetosphere-ionosphere coupling. Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18030402 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 402 Subjects: – SubjectFull: Auroras Type: general – SubjectFull: Video processing Type: general – SubjectFull: Automatic identification Type: general – SubjectFull: Space sciences Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: Automatic Identification and Segmentation of Diffuse Aurora from Untrimmed All-Sky Auroral Videos. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Qian – PersonEntity: Name: NameFull: Hao, Peiqi – PersonEntity: Name: NameFull: Pan, Han IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 3 Titles: – TitleFull: Remote Sensing Type: main |
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