Zhai, Chaoqiang and Wang, Qian (2026) Retrieving rare aurora forms from all-sky images via synthetic-to-real progressive learning. Advances in Polar Science, 37 (1). pp. 70-80.
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Abstract
Fine-scale structures can be observed in small field-of-view (FOV) auroral observations, but they are often overlooked because they appear only sporadically in all-sky observations. Such forms are of great interest because they may embody specific magnetosphere-ionosphere coupling processes, reveal localized energy deposition pathways, and provide new insights into cross-scale plasma dynamics and instabilities. However, their limited spatial extent, transient occurrence, and scarcity in wide-FOV observations make systematic investigation challenging. Traditional manual analysis struggles to capture these subtle structures within vast all-sky datasets, while automated detection faces severe data imbalance and morphological ambiguity. To address these challenges, we propose a synthetic-to-real progressive learning framework for cross-FOV retrieval of rare auroral forms. A Generative Adversarial Network (GAN) is employed to perform cross-FOV transformation between unpaired small-FOV images containing rare aurora forms and all-sky images (ASI) without such structures, thereby generating large numbers of synthetic ASI with rare auroral morphology. These synthetic samples are used to train an initial detection model, which subsequently undergoes iterative fine-tuning through feedback-guided learning: The model performs inference on new all-sky data, and the progressively accumulated real detections are incorporated into the training set. Experimental results demonstrate that the proposed method achieves over 92% detection accuracy on ASI, enabling high-precision retrieval of small-scale auroral structures across large-scale observations. This framework provides a scalable and effective approach to rediscovering rare auroral phenomena in continuous all-sky monitoring, offering new opportunities for exploring the fine-scale dynamics of the upper atmosphere.
| Item Type: | Article |
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| Keywords: | fine-scale auroral structures, rare auroral forms, cross-FOV retrieval, Generative Adversarial Network (GAN), synthetic-to-real progressive learning, feedback-guided learning |
| Subjects: | Natural Environment > Space Peoples, Cultures and Societies > Media |
| Organizations: | Advances in Polar Science (APS) |
| Date Deposited: | 24 Apr 2026 10:18 |
| URI: | https://library.arcticportal.org/id/eprint/2962 |
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