Auroral Image Classification With Very Limited Labeled Data Using Few-Shot Learning

Abstract

Auroral image classification is an important task for polar research. At present, the auroral image classification mainly focuses on common auroral types with a lot of labeled samples, where the classification performance is heavily dependent on large labeled datasets for supervised training. However, in many instances, such as for rare auroras or newly discovered auroras, it is not feasible to obtain ample category labels. This letter proposes a few-shot learning-based solution for auroral image classification with very limited labeled samples. Since auroral data are always collected by ground-based all-sky imagers with little domain differences, we incorporate prior knowledge into auroral image representation with limited labeled samples using the labels from previous auroral image classification studies. Also, the cosine classifier is employed to avoid the overfitting problem caused by the very few labels of the auroral images to be classified. When providing one and five labeled samples for each category, our solution achieves an average classification accuracy of 87.14% and 95.33% on the novel auroral category dataset, respectively. The effectiveness of this solution is further demonstrated by auroral image retrieval and temporal occurrence distributions. The experimental results show that the proposed solution provides a new idea for the automatic classification and retrieval of auroral images, especially for those cases where there are only very few labeled samples.