Progressively Coding the Human Visual Information for Auroral Substorm Recognition

For a long time, selecting specific events from a large number of auroral sequences and analyzing them has been a routine process for space physics experts to study space activities between the Sun and the Earth. As a common space event with greater impact on the magnetosphere and ionosphere, substorms are a hot topic in the space physical field. Therefore, the automated identification of substorm sequences is an ongoing task. At the same time, due to the particularity of substorm sequence data, people who do not belong to the field of space physics cannot label and identify them, which largely limits the application of artificial intelligence techniques in this field. Therefore, this paper proposes a progressive method for encoding visual information to automatically identify substorm sequences. Based on a small amount of substorm visual map labeled by manually, this method designs a stream to learn and predict these visual maps. Meanwhile, the predicted visual map will assist the network in extracting representative sequence features and finally getting the identification results of substorm sequences automatically. The series of experimental results show the effectiveness of the proposed method.