Solar Filament Recognition Based on Deep Learning

TL;DR

This paper introduces a deep learning-based method using U-Net for automatic and accurate recognition of solar filaments in H-alpha images, effectively reducing noise effects and validated on real observational data.

Contribution

The paper develops a novel automated solar filament recognition method leveraging deep learning, specifically U-Net, with a large dataset and validation on real solar images.

Findings

High accuracy in filament identification

Effective noise reduction in images

Validated on real observational data

Abstract

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.