太阳射电频谱爆发识别的元学习方法

太阳射电频谱图像在太阳活动和空间天气的观测、研究和预报中有着重要的作用。太阳射电宽带动态频谱仪是国内观测太阳射电信号的主要设备, 但受到窗口时间、观测设备和太阳活动规律的影响, 其所采集到的频谱数据存在有效样本量少的问题。针对这一现状, 提出了一种基于元学习和迁移学习的少样本学习方法, 用于改善太阳射电频谱图像的分类性能。首先模型在元学习基准数据集上进行元知识的学习, 然后对射电频谱图像进行小样本识别的模型定义, 最后将元知识迁移到频谱图像数据集的分类任务中。通过对多种元学习方法进行实验分析和性能比较, 证明了本文方法的先进性和有效性。

Meta-learning method for solar radio spectrum burst recognition

Solar radio spectrum images play an important role in the observation, research and prediction of solar activity and space weather. The solar radio broadband dynamic spectrometer is the main equipment for observing solar radio signals in China. However, it is affected by the window time, observation equipment and the law of solar activities, and the collected spectrum data has the problem of few effective samples. In view of this situation, a few-shot learning method based on meta-learning and transfer-learning is proposed to improve the classification performance of solar radio spectrum images. First, the model learns meta-knowledge on the meta-learning benchmark dataset, then defines the model for a few sample recognition of radio spectrum images, and finally transfers the meta-knowledge to the classification task of the spectrum image dataset. Through experimental analysis and performance comparison of multiple meta-learning methods, it proves that the method in this paper is advanced and effective.