Solar-Filament Detection and Classification Based on Deep Learning

Solar filaments are distinct strip-like structures observed in chromospheric H α images. Filament eruptions, flares, and coronal mass ejections (CMEs) can be regarded as the same physical process of releasing magnetic energy at different times and solar atmosphere heights. It is very important to detect filaments for forecasting flares and CMEs. This article proposes a new solar-filament detection and classification method based on CondInst; a deep-learning model. A data set of solar filaments is built, including ten thousand filaments. To distinguish filaments that consist of only a single connected dark region and filaments that are broken into several fragments, the filaments are classified into isolated filaments and non-isolated filaments. The mean precision, recall, A P , and F1 obtained using the proposed method are 90.83 % , 83.88 % , 82.86 % , and 87.22 % , respectively. The results show that the method performs well in detecting and classifying isolated and non-isolated filaments, especially in solving the fragments problem of how to detect a filament that is broken into several fragments. The method also has good performance in handling various images, even with existing uneven brightness or low contrast. The precision of filament masks still needs to be improved in the future.