Solar Filament Recognition Based on Deep Learning
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.
KeywordsFilaments Prominences Image processing Deep learning
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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