基于多尺度多模态学习的光球亮点曲线轨迹段检测方法研究

Curvilinear trajectory detection for photosphere bright points based on multi-scale and multi-modal learning

太阳光球亮点近似旋转的曲线运动对研究太阳内部的能量如何传输到日冕层具有重要意义。现有的算法仅能检测光球亮点的全局型曲线运动,因此提出了一种多尺度多模态的深度学习方法来检测光球亮点的全局型和局部型曲线运动。首先,基于双向长短期记忆网络构建了一种多尺度网络模型,用来提取光球亮点的运动轨迹段的多尺度时序特征;然后,采用EfficientNet-B0提取运动轨迹段的空间特征,通过将时序特征和空间特征融合成多模态特征来检测光球亮点各种类型的曲线轨迹段。实验结果表明,所提方法的准确率达到了85.08%,相较于单尺度方法的提升了6.12%,相较于多尺度单模态方法的提升了3.1%。所提方法亦可应用于其他领域的运动类型检测任务中。

The curvilinear motion of solar photosphere bright points,which is approximate rotation,is of great significance for studying how the energy from solar convection zone is transmitted to the corona.The existing algorithms only detect the global curvilinear motion of photosphere bright points.This paper proposes a multi-scale and multi-modal deep learning method to detect the global and local curvilinear motion of photosphere bright points.This mothod constructs a multi-scale network model based on the bidirectional long short-term memory network(Bi-LSTM)to extract multi-scale time sequence features of the trajectories of photosphere bright points.EfficientNet-B0 is adopted to extract the spatial features of the trajectories.The temporal features and spatial features are fused into multi-modal features to detect various curvilinear motions of photosphere bright points.The experiment results show that the accuracy of this method is 85.08%,which is 6.12%higher than that of the single-scale method and 3.1%higher than that of the multi-scale and single-mode method.This method can also be applied to the motion type detection requirements in other fields.