提出一种基于深度学习的极光序列分类方法,有效结合卷积神经网络(CNN)特征丰富的空间域信息和长短时记忆(LSTM)网络捕捉序列信息的优势,并利用极光的属性对CNN添加反馈约束调节使特征更契合极光图像。在中国北极黄河站的全天空成像仪(ASI)极光图像数据库上进行有监督的极光序列分类研究和无监督的极光事件检测,实验结果表明本文方法能有效用于极光序列的表征,为海量极光序列的自动分类提供了可能性。

An aurora sequence classification method based on deep learning is proposed. It combines the rich spatial domain information and the sequence information corresponding to the advantages of convolutional neural network (CNN) features and long short-term memory (LSTM) network. In addition, aurora attributes employed as feedback constraints to the CNN make features more suitable for aurora images. Supervised aurora sequence classification and unsupervised aurora event detection are performed on the Chinese Yellow River Station All-Sky Imager (ASI) dataset. The experiment shows that our method can characterize aurora sequences effectively and can be able to implement automatic classification for massive aurora sequences.