发表机构
School of Astronomy and Space Science, Nanjing University; Institute of Science and Technology for Deep Space Exploration, Suzhou Campus, Nanjing University（南京大学天文与空间科学学院; 南京大学苏州校区深空探测科学技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对太阳暗条检测面临的挑战，提出系列机器学习方法，通过标注数据集、开发MORDEN模型及引入后处理方法，生成高质量数据集，实验表明该方法性能良好，为太阳暗条检测深度学习模型潜力最大化奠定基础。
AI 中文摘要
利用深度学习进行自动太阳暗条检测面临诸多挑战。太阳暗条的语义分割是一项复杂的多尺度特征提取任务，且具有长尾分布。此外，大规模、高度完整且精细详细的数据集对于提供丰富信息至关重要。为应对这些挑战，我们提出一系列机器学习方法来开发出色的太阳暗条检测工作流程。首先手动标注了基于Hα光谱的小规模太阳暗条数据集MHAS，接着开发了专注多尺度特征提取的语义分割模型MORDEN，还引入DenseCRF和DBSCAN方法进行后处理，生成了大规模高质量数据集AHAS。实验结果表明MORDEN优于现有开源模型，DenseCRF能有效捕捉精细边缘细节，评估了数据缩放及DBSCAN的效果，可视化结果证实了定量发现。我们的工作为最大化深度学习模型在太阳暗条检测中的潜力奠定基础。
英文摘要
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 multiscale feature extraction. We also introduced the Dense Conditional Random Field (DenseCRF) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methods for post-processing. Using the proposed workflow, we generated a large-scale, high-quality dataset called AHAS. Experimental results demonstrate that MORDEN outperforms several existing solar filament semantic segmentation models with open access. DenseCRF has been demonstrated to effectively capture fine edge details. We also evaluated the effects of data scaling and the reliability of DBSCAN and found that both approaches yield satisfactory performance. Multiple visualization results substantiate our quantitative findings. Our work provides a foundation for maximizing the potential of deep learning models for solar filament detection.
Comments21 pages, 4 figures, accepted for publication in RAA
