1. SCSS-Net: solar corona structures segmentation by deep learning
- Author
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Peter Butka, Viera Maslej-Krešňáková, Martin Harman, and S. Mackovjak
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Extreme ultraviolet lithography ,FOS: Physical sciences ,Coronal hole ,Space weather ,Convolutional neural network ,Machine Learning (cs.LG) ,Physics - Space Physics ,Astrophysics::Solar and Stellar Astrophysics ,Segmentation ,Neural and Evolutionary Computing (cs.NE) ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,Solar and Stellar Astrophysics (astro-ph.SR) ,Remote sensing ,Physics ,business.industry ,Deep learning ,Software development ,Computer Science - Neural and Evolutionary Computing ,Astronomy and Astrophysics ,Space Physics (physics.space-ph) ,Astrophysics - Solar and Stellar Astrophysics ,Space and Planetary Science ,Physics::Space Physics ,Artificial intelligence ,Astrophysics - Instrumentation and Methods for Astrophysics ,business ,Transfer of learning - Abstract
Structures in the solar corona are the main drivers of space weather processes that might directly or indirectly affect the Earth. Thanks to the most recent space-based solar observatories, with capabilities to acquire high-resolution images continuously, the structures in the solar corona can be monitored over the years with a time resolution of minutes. For this purpose, we have developed a method for automatic segmentation of solar corona structures observed in EUV spectrum that is based on a deep learning approach utilizing Convolutional Neural Networks. The available input datasets have been examined together with our own dataset based on the manual annotation of the target structures. Indeed, the input dataset is the main limitation of the developed model's performance. Our \textit{SCSS-Net} model provides results for coronal holes and active regions that could be compared with other generally used methods for automatic segmentation. Even more, it provides a universal procedure to identify structures in the solar corona with the help of the transfer learning technique. The outputs of the model can be then used for further statistical studies of connections between solar activity and the influence of space weather on Earth., Comment: accepted for publication in Monthly Notices of the Royal Astronomical Society; for associated code, see https://github.com/space-lab-sk/scss-net
- Published
- 2021
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