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Retrieving rare aurora forms from all-sky images via synthetic-to-real progressive learning

ZHAI Chaoqiang WANG Qian

极地科学进展（英文版）2026，Vol.37Issue(1)：70-80,11.

极地科学进展（英文版）2026，Vol.37Issue(1)：70-80,11.DOI:10.12429/j.advps.2025.0042

Retrieving rare aurora forms from all-sky images via synthetic-to-real progressive learning

ZHAI Chaoqiang ¹WANG Qian¹

作者信息

1. School of Communication and Information Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China
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摘要

关键词

fine-scale auroral structures/rare auroral forms/cross-FOV retrieval/Generative Adversarial Network(GAN)/synthetic-to-real progressive learning/feedback-guided learning

Key words

fine-scale auroral structures/rare auroral forms/cross-FOV retrieval/Generative Adversarial Network(GAN)/synthetic-to-real progressive learning/feedback-guided learning

引用本文复制引用

ZHAI Chaoqiang,WANG Qian..Retrieving rare aurora forms from all-sky images via synthetic-to-real progressive learning[J].极地科学进展（英文版）,2026,37(1):70-80,11.

基金项目

This work was supported by the National Natural Science Foundation of China(Grant no.41874173). （Grant no.41874173）

极地科学进展（英文版）

ISSN：1674-9928

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