Cross-Station Continual Aurora Image Classification

Yanfei Zhong, Jingjun Yi, Richen Ye, Liangpei Zhang

Abstract: The existing deep learning-based methods have shown great potential for the aurora image classification problem. However, there are many differences in the morphology and distribution patterns of aurora images from different observation stations, and the differences between Antarctic and Arctic aurora images are particularly obvious. Currently, there are 76 research stations in 31 countries in Antarctica and more than 100 land-based stations in the Arctic. In the face of the difference in morphology and distribution patterns between Antarctic and Arctic auroras, the current popular methods cannot maintain a consistent classification ability. At the same time, it is important to effectively use both historical and real-time information to enable continual learning of aurora classification models to take full advantage of the high temporal resolution of streaming aurora image data. In this article, a cross-station continual (CSC) aurora image classification framework is proposed to tackle these problems. To simulate a cross-station aurora image data stream, aurora images from three observation stations located in the Antarctic and the Arctic were selected and split into mini-batches in a chronological order to form the cross-station streaming (CSS) aurora image dataset. Based on the vision transformer (ViT) model, the CSC framework sequentially learns the semantic representation of streaming aurora data by learning dynamic prompts in the prompt bank selected by the average cosine distance. For the cross-station aurora discrepancy phenomenon, a local–global enhancement (LGE) module is designed, by organically combining the local and global semantics of aurora images to reduce the microscopic intraclass similarity and macroscopic interclass confusion. Extensive experiments conducted on the CSS dataset show that the proposed method can achieve efficient continual learning of streaming aurora data and a competitive classification accuracy under the condition of joint training of data from multiple observation stations.

External IDs:dblp:journals/tgrs/ZhongYYZ24