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Solar Physics

, 294:117 | Cite as

Solar Filament Recognition Based on Deep Learning

  • Gaofei ZhuEmail author
  • Ganghua Lin
  • Dongguang Wang
  • Suo Liu
  • Xiao Yang
Article

Abstract

The paper presents a reliable method using deep learning to recognize solar filaments in H\(\upalpha\) full-disk solar images automatically. This method cannot only identify filaments accurately but also minimize the effects of noise points of the solar images. Firstly, a raw filament dataset is set up, consisting of tens of thousands of images required for deep learning. Secondly, an automated method for solar filament identification is developed using the U-Net deep convolutional network. To test the performance of the method, a dataset with 60 pairs of manually corrected H\(\upalpha\) images is employed. These images are obtained from the Big Bear Solar Observatory/Full-Disk H-alpha Patrol Telescope (BBSO/FDHA) in 2013. Cross-validation indicates that the method can efficiently identify filaments in full-disk H\(\upalpha\) images.

Keywords

Filaments Prominences Image processing Deep learning 

Notes

Acknowledgements

The work was funded by National Science Foundation of China (Grant Nos: u1531247 and 11427901), the 13th Five-year Informatization Plan of Chinese Academy of Sciences, (Grant No. XXH13505-04), and the special foundation work of the Ministry of Science and Technology of China (Grant No: 2014fy120300). We thank BBSO for providing full-disk H\(\upalpha\) images for the experiment.

Disclosure of Potential Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.National Astronomical ObservatoriesChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Solar ActivityNational Astronomical ObservatoriesBeijingChina
  4. 4.School of Astronomy and Space SciencesUniversity of Chinese Academy of SciencesBeijingChina

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