Solar Active Regions Detection and Tracking Based on Deep Learning
Author:
Funder
National Natural Science Foundation of China
Yunnan Key Research and Development Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11207-024-02362-3.pdf
Reference45 articles.
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2. Bernardin, K., Stiefelhagen, R.: 2008, Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008, 1.
3. Bobra, M.G., Wright, P.J., Sun, X., Turmon, M.J.: 2021, Smarps and sharps: two solar cycles of active region data. Astron. Astrophys. Suppl. Ser. 256, 26.
4. Caballero, C., Aranda, M.: 2014, Automatic tracking of active regions and detection of solar flares in solar EUV images. Solar Phys. 289, 1643.
5. Colak, T., Qahwaji, R.: 2009, Automated solar activity prediction: a hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares. Space Weather 7.
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