极地研究2018，Vol.30Issue(2)：123-131,9.DOI:10.13679/j.jdyj.20170038
基于卷积神经网络的极光图像分类
CLASSIFICATION OF AURORAL IMAGES BASED ON CONVOLUTIONAL NEURAL NETWORK
摘要
Abstract
Auroral light is the result of charged particles interacting with the magnetosphere and ionosphere. Proper classification of complex morphological all-sky auroral images is meaningful for studying the rela-tionship between electromagnetic activity and energy coupling. To address these issues, a method of deep learning based on a convolutional neural network was proposed to explore the feature space of auroral data and to achieve automatic auroral recognition. The representation method was used in automatic recognition of four primary categories of aurora observed in 2003 at the Yellow River Station. The supervised classifica-tion accuracy rates on labeled data between dataset1 and dataset2 were 93.17％ and 91.5％, respectively. The occurrence distributions of the four categories obtained through automatic classification of data from 2004–2009, were consistent with the spectral energy distribution excited by three bands. The experimental results showed that the presented representation method is effective for automatic auroral image recognition.关键词
极光/卷积神经网络/分类Key words
aurora/convolutional neural network/classification引用本文复制引用
王菲,杨秋菊..基于卷积神经网络的极光图像分类[J].极地研究,2018,30(2):123-131,9.基金项目
国家自然科学基金(41504122)、陕西省自然科学青年人才项目(2016JQ4001)、陕西省高校科协青年人才托举计划(20160211)资助 （41504122）
