| Summary: | Identification of small-scale auroral structures is key to searching for auroral events. However, it is impracticable for humans to manually select sufficient aurora events for statistical analysis, and it is also challenging for computers because of the non-rigid shape and fluid nature of auroras. Fold structure is the most common type of auroral small-scale structure, and its appearance is indicative of a variety of auroral events. This paper proposes a small-scale aurora structure identification framework to automatically detect aurora fold structures. First, the location and shape of auroras are identified based on a deep learning segmentation network. Then, the skeleton of the auroral shape is extracted to represent the trajectories of auroras. Finally, the proposed skeleton-based fold identification module (SFIM) can detect the aurora fold structure. To evaluate the effectiveness of the proposed method, we built an aurora fold structure sample dataset, namely F-dataset, containing 2000 images at 557.7 nm obtained by the all-sky imagers at Yellow River Station (YRS), Ny-Ålesund, Svalbard. Experimental results show that automatic identification can achieve good consistency with human perception. Statistical analysis of over 30,000 images shows that the fold occurrence has a distinct double-peak distribution at pre-noon and post-noon.
|
|---|