Solar Filament Recognition Based on Deep Learning

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, c...

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Bibliographic Details
Published in:arXiv.org 2019-09
Main Authors: Zhu, GaoFei, Lin, GangHua, Wang, DongGuang, Liu, Suo, Yang, Xiao
Format: Article
Language:English
Subjects:
Datasets
Deep learning
Filaments
Machine learning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1909.06580