Solar Filament Segmentation Based on Improved U-Nets

To detect, track and characterize solar filaments more accurately, novel filament segmentation methods based on improved U-Nets are proposed. The full-disk Hα images from the Huairou Solar Observing Station of the National Astronomical Observatory and the Big Bear Solar Observatory were used for training and verifying the effectiveness of different improved networks’ filament segmentation performance. Comparative experiments with different solar dataset sizes and input image quality were performed. The impact of each improvement method on the segmentation effect was analyzed and compared based on experimental results. In order to further explore the influence of network depth on filament-segmentation accuracy, the segmentation results produced by Conditional Generative Adversarial Networks (CGAN) were obtained and compared with improved U-nets. Experiments verified that U-Net with an Atrous Spatial Pyramid Pooling Module performs better for high-quality input solar images regardless of dataset sizes. CGAN performs better for low-quality input solar images with large dataset size. The algorithm may provide guidance for filament segmentation and more accurate segmentation results with less noise were acquired.