A Modern ConvNet for Solar Filament Detection
Automated solar filament detection using deep learning faces several challenges. Semantic segmentation of solar filaments is a complicated multiscale feature extraction task with long-tail distribution. Furthermore, a large-scale, highly complete, and finely detailed dataset has become mandatory for providing abundant information. To address these challenges, we present a series of machine learning approaches to develop a solar filament detection workflow that performs superbly. First, we manually annotated a small-scale solar filament dataset based on H$α$ spectra called MHAS. Next, we developed the Multiscale ORiented DENdritic (MORDEN) model, a semantic segmentation model focusing on m...
| Authors | J. R. Hu, Q. Hao, Z. Zheng et al. |
|---|---|
| Primary category | astro-ph.SR |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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