  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.

Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics