Replication Data for: Auroral Image Classification with Deep Neural Networks

Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral images in an objective, organized and repeatable manner. Although previous studies have presented tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a high precision (>90%). This work considers seven auroral subclasses; breakup, colored, arcs-bands, discrete, patchy, edge and clear-faint. Five different deep neural network architectures have been tested along with the well known classification methods; k nearest neighbor (KNN) and support vector machine (SVM). A set of clean nighttime color auroral images, without ambiguous auroral forms, moonlight, twilight, clouds etc., were used for training and testing. The deep neural networks generally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highest performance with an average classification precision of 92%. Although the results indicate that high precision aurora classification is an attainable objective using deep neural networks, it is stressed that a common consensus of the auroral morphology and the criteria for each class needs.The authors would like to thank Urban Brändström and the Swedish Institute of Space Physics for providing the original auroral image data. The image data archive is freely accessible at http://www2.irf.se/allsky/data.html, however, the users are obliged to contact the Kiruna Atmospheric and Geophysical Observatory before usage

本研究展示了基于机器学习技术的极光自动分类研究成果。极光是电离层-磁层环境中物理现象的直观表征。对南北极数百万幅极光图像开展自动分类，可为构建极光统计数据库提供有效工具，同时能够支持科研人员以客观、规范且可复现的方式开展极光图像研究。尽管既往已有研究提出了极光检测工具，但目前仍缺乏可将极光精准分类至子类且精度高于90%的分类方案。本研究涵盖7类极光子类别：爆发型（breakup）、彩色型（colored）、弧带型（arcs-bands）、离散型（discrete）、斑块型（patchy）、边缘型（edge）以及清晰弱光型（clear-faint）。研究对5种不同的深度神经网络架构，结合经典分类算法k近邻（KNN）与支持向量机（SVM）进行了测试。训练与测试数据集采用一批无干扰的夜间彩色极光图像，排除了模糊极光形态、月光、暮光、云层等干扰因素。总体而言，深度神经网络的分类性能优于KNN与SVM算法，其中ResNet-50架构表现最优，平均分类精度达92%。尽管研究结果表明，利用深度神经网络可实现高精度极光分类，但需强调的是，当前仍需就极光形态学特征及各类别判定标准达成普遍共识。作者感谢Urban Brändström与瑞典空间物理研究所（Swedish Institute of Space Physics）提供原始极光图像数据。该图像数据集存档可通过http://www2.irf.se/allsky/data.html免费获取，但用户在使用前需联系基律纳大气与地球物理观测台（Kiruna Atmospheric and Geophysical Observatory）。