基于双层聚类信息的极光亚暴自动检测  

An auroral substorm detection method based on cascaded cluster algorithms

作　　者：王平[1] 韩冰[2] 李洁[2] 胡泽骏 尚军亮[1] 葛道辉 袁玉卓 WANG Ping;HAN Bing;LI Jie;HU Zejun;SHANG Junliang;GE Daohui;YUAN Yu-zhuo(School of Computer Science,Qufu Normal University,Rizhao 276800,China;School of Electronic Engineering,Xidian University,Xi’an 710071,China;Key Laboratory of Polar Science,MNR,Polar Research Institute of China,Shanghai 200136,China)

机构地区：[1]曲阜师范大学计算机学院,日照276800 [2]西安电子科技大学电子工程学院,西安710071 [3]自然资源部极地科学重点实验室,中国极地研究中心(中国极地研究所),上海200136

出　　处：《极地研究》2024年第1期52-69,共18页Chinese Journal of Polar Research

基　　金：国家自然科学基金面上项目(62076190,41831072);陕西省重点产业创新链(2022ZDLGY01-11);山东省自然科学基金青年项目(ZR2023QF068)资助。

摘　　要：极光亚暴与太阳风和地球磁场的耦合过程有着紧密的联系,对其发生和发展机制的研究,有助于深入地分析行星际磁场、地球磁层和地球电离层的相互作用,了解太阳风携带的大量能量在地球空间的输运过程,对地球空间环境预警具有重要的意义。Polar卫星搭载的紫外极光图像成像仪能够全天候地获取紫外极光图像,在紫外极光图像中可以完整地展示出极区极光的亮度和尺度变化,尤其是可以清晰地展示出亚暴膨胀相的极光点亮和亮斑膨胀现象。现有的极光亚暴事件检测方法通常需要人工设计特征和相关规则库,耗时耗力。本文利用紫外极光图像提出了基于双层聚类信息的亚暴事件检测方法,实现了紫外极光图像数据中的亚暴事件自动检测。同时,针对极光亚暴事件检测依赖手工设计特征,设计了子空间聚类指导的三维卷积特征自动提取网络;针对极光图像帧间存在成像角度差异,利用地磁纬度和磁地方时信息对极光序列中图像的空间位置进行校正;针对卫星成像位置变化导致的成像噪声,利用极光图像级聚类保留极光亮斑区域和剔除未成像或噪声区域。主观和客观实验结果表明,本算法提升了亚暴事件检测的查全率。The breakup of the auroral substorm is closely related to a sudden electromagnetic energy release dur-ing solar wind-magnetosphere coupling process.Understanding the mechanisms of substorm onset and ex-pansion phase clarifies the interactions among interplanetary magnetic field,magnetosphere and ionosphere.Additionally,substorm research is essential to characterize the process of flux transport from the solar to earth,which is significant to the space weather forecast.Auroral images from the ultraviolet imager(UVI)aboard the Polar satellite are the main dataset containing records of auroral substorms,with clear depiction of complete auroral ovals and substorm bulge features.Existing substorm detection algorithms are mostly empirical,relying on manually designed features and rules.In this article,we propose a detection algorithm guided by cascaded cluster algorithms for automatic substorm detection.To avoid using handcraft features,spatiotemporal features of UVI image sequences are extracted using a three-dimensional convolution net-work with subspace clustering.Because of imaging angles differences between frames,UVI images coordi-nates are converted into MLAT-MLT(the magnetic latitude-magnetic local time)coordinate system for pixel alignment.Moreover,image level clustering is applied to reduce the UVI image noise by isolating the sub-storm bulge and discarding unimaged areas.Experimental results indicate that the proposed method achieves higher recall than existing standard methods.

关 键 词：极光亚暴 双层聚类信息 三维卷积网络 自动检测 

分 类 号：P353[天文地球—空间物理学]