Solar Filament Recognition Based on Deep Learning

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.

13 pages, 7 figures, 2 tables, accepted for publication in Solar Physics

References in corpus (4)

• Observation of An Evolving Magnetic Flux Rope Prior To and During A Solar Eruption
• Development of an Advanced Automated Method for Solar Filament Recognition and Its Scientific Application to a Solar Cycle of MLSO Hα Data
• Automatic Detection of Limb Prominences in 304 A EUV Images
• Initiation and propagation of coronal mass ejections

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• Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk H$\alpha$ Images
• Automatic detection of Ellerman bombs using Deep Learning
• A Robust Deep Learning Framework for Prominence Detection through Composite Feature Representations
• Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H$\alpha$ Images