SCSS-Net: Solar Corona Structures Segmentation by Deep Learning

TL;DR

SCSS-Net is a deep learning-based method for automatic segmentation of solar corona structures in EUV images, aiding space weather research by providing a universal and transferable approach.

Contribution

The paper introduces SCSS-Net, a novel deep learning model that automates segmentation of solar corona structures, utilizing transfer learning for improved adaptability.

Findings

Effective segmentation of coronal holes and active regions.

Comparison with existing methods shows competitive performance.

Transfer learning enhances model generalization.

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.