The paper presents a reliable method using deep learning to recognize solar filaments in H-alpha 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-alpha 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-alpha images.
@article{arxiv.1909.06580,
title = {Solar Filament Recognition Based on Deep Learning},
author = {GaoFei Zhu and GangHua Lin and DongGuang Wang and Suo Liu and Xiao Yang},
journal= {arXiv preprint arXiv:1909.06580},
year = {2019}
}
Comments
13 pages, 7 figures, 2 tables, accepted for publication in Solar Physics