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发表刊物：Journal of Atmospheric and Solar-Terrestrial Physics
关键字：POLEWARD BOUNDARY; INTENSIFICATIONS; Convolutional neural networks
摘要：Convolutional neural networks (CNNs) in deep learning enable the extraction of features in image data. Through the multi-layer superposition of a convolutional neural network, we can better capture the essential characteristics of different auroral subclasses and further classify auroral images in detail. Because the auroral morphological features often present abstract characteristics, our study compares different CNN architectures and different layering in order to test the best neural network model for mesoscale aurora classification. Although the classification models and subclasses used b
合写作者：J. -Y. Yang,+++,L. -Y. Li ,+++
第一作者：Z.-X. Guo
论文类型：基础研究
论文编号：105906
一级学科：地球物理学
文献类型：期刊
卷号：235
页面范围：DOI10.1016/j.jastp.2022.105906
ISSN号：1364-6826
是否译文：否
发表时间：2022-06-21
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S1364682622000797?via%3Dihub