Journal of Frontiers of Computer Science and Technology ›› 2023, Vol. 17 ›› Issue (1): 187-197.DOI: 10.3778/j.issn.1673-9418.2105044

• Artificial Intelligence·Pattern Recognition • Previous Articles     Next Articles

Auroral Substorm Event Recognition Method Combining Eye Movement Infor-mation and Sequence Fingerprint

HAN Yiyuan, HAN Bing, GAO Xinbo   

  1. 1. School of Electronic Engineering, Xidian University, Xi’an 710071, China
    2. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chong-qing 400065, China
  • Online:2023-01-01 Published:2023-01-01



  1. 1. 西安电子科技大学 电子工程学院,西安 710071
    2. 重庆邮电大学 重庆市图像认知重点实验室,重庆 400065

Abstract: The occurrence process, effects, and models of the auroral substorm phenomenon has been one of the most important frontier topics in solar-terrestrial physics in past 30 years. The ultraviolet imager (UVI) carried by the polar satellite collects a very large number of ultraviolet auroral images every year. Automatically and accurately identifying substorm events from massive UVI images is an urgent problem in this field. At present, there are some studies on the detection and recognition of auroral substorm events. These methods effectively use the physical characteristics of substorm events, but none of them use expert visual cognition information as a priori for substorm events recognition task. Therefore, in this paper, eye movement information is used as a visual and intuitive repre-sentation of expert knowledge, and combined with the physical characteristics of the auroral substorm sequences, an auroral substorm event recognition method combining eye movement information and sequence fingerprint is proposed. Firstly, this paper uses the eye tracker to obtain the eye movement information of space physics experts on the auroral substorm sequence. Secondly, substorm events are marked according to their physical characteristics to obtain the sequence fingerprint of each auroral substorm event. Finally, the recognition of substorm events is identified based on expert eye movement information and sequence fingerprint identification strategy. At the same time, experiments show the effectiveness of the proposed method.

Key words: auroral substorms, visual cognitive, eye movement information, sequence fingerprint, sequence recognition

摘要: 极光亚暴的发生过程、效应和模型的研究一直是近30年来日地物理学中最受重视的核心前沿课题之一。Polar卫星携带的紫外成像仪(UVI)每年采集到的紫外极光图像数量非常庞大,自动且精确地从海量的紫外极光图像中识别亚暴事件是该领域亟需解决的问题。目前已经有针对极光亚暴事件检测、识别等方面的研究,这些方法均有效利用了亚暴事件的物理特性,但都没有利用专家视觉认知信息作为先验。因此,以眼动信息作为专家知识的一种视觉直观表示,结合亚暴序列本身的物理特性,提出了结合眼动信息和序列指纹的亚暴事件识别方法。首先,利用眼动仪获取空间物理专家对极光亚暴序列认知的眼动信息;其次,根据亚暴事件的物理特征对其进行标记得到每个极光亚暴事件的序列指纹;最后,利用专家的眼动信息与序列指纹识别策略实现对亚暴事件的自动识别。同时,实验也表明了所提出的方法的有效性。

关键词: 极光亚暴, 视觉认知, 眼动信息, 序列指纹, 事件识别