Volume 43 Issue 2
Mar.  2023
Turn off MathJax
Article Contents
Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(2): 219-230 Open Access
ZHANG Lingshu, ZOU Ziming, BAI Xi. Clustering of Ultraviolet Auroral Oval Images Based on Deep Representation Learning (in Chinese). Chinese Journal of Space Science, 2023, 43(2): 219-230 doi: 10.11728/cjss2023.02.220127012
Citation: ZHANG Lingshu, ZOU Ziming, BAI Xi. Clustering of Ultraviolet Auroral Oval Images Based on Deep Representation Learning (in Chinese). Chinese Journal of Space Science, 2023, 43(2): 219-230 doi: 10.11728/cjss2023.02.220127012
shu

Clustering of Ultraviolet Auroral Oval Images Based on Deep Representation Learning

doi: 10.11728/cjss2023.02.220127012 cstr: 32142.14.cjss2023.02.220127012
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • 3.

    National Space Science Data Center, Beijing 100190

  • Received Date: 2022-01-27
  • Accepted Date: 2022-05-20
  • Rev Recd Date: 2022-11-07
    • Available Online: 2023-02-13
    Abstract
  • Aurora is affected by large-scale dynamics such as geomagnetic substorm driven by solar wind, due to varies solar wind-magnetosphere-ionosphere coupling effects, its morphology and evolution can be different. Currently, categorization of aurora oval and its morphology is mostly based on auroral evolution theory to do subjective qualitative analysis and has no clear classification standard, which makes it challenging to conduct objective quantitative research with statistical analysis method and supervised classification models. Ultraviolet (UV) auroral oval image clustering model (MoCo-GMM) was established based on deep representation learning, also a method was designed to evaluate physical rationality of the model by using space environment parameters. Additionally, experiments on large-scale POLAR UV auroral oval image data were carried out. Clustering results of MoCo-GMM obtained not only delightful intra-cluster cohesion and inter-cluster separation, but a certain degree of physical interpretability, which means we effectively realized objective categorization of aurora oval and its morphology based on images.

     

    • FullText(HTML)
  • loading
    • References(22)
  • [1]
    KEIKA K, NAKAMURA R, BAUMJOHANN W, et al. Substorm expansion triggered by a sudden impulse front propagating from the dayside magnetopause[J]. Journal of Geophysical Research: Space Physics, 2009, 114(52): A00C24
    [2]
    BOUDOURIDIS A, ZESTA E, LYONS R, et al. Effect of solar wind pressure pulses on the size and strength of the auroral oval[J]. Journal of Geophysical Research: Space Physics, 2003, 108(A4): 8012 doi: 10.1029/2002JA009373
    [3]
    YANG Q J, WANG Y Y, REN J. Auroral image classification with very limited labeled data using few-shot learning[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 6506805
    [4]
    KVAMMEN A, WICKSTRØM K, MCKAY D, et al. Auroral image classification with deep neural networks[J]. Journal of Geophysical Research: Space Physics, 2020, 125(10): e2020JA027808
    [5]
    YANG Q J, ZHOU P H. Representation and classification of auroral images based on convolutional neural networks[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 523-534 doi: 10.1109/JSTARS.2020.2969245
    [6]
    AKASOFU S I. Auroral morphology: a historical account and major auroral features during auroral substorms[M]//KEILING A, DONOVAN E, BAGENAL F, et al. Auroral Phenomenology and Magnetospheric Processes: Earth and Other Planets. Washington DC: American Geophysical Union, 2012: 29-38
    [7]
    HENDERSON M G. Auroral substorms, poleward boundary activations, auroral streamers, omega bands, and onset precursor activity[M]//KEILING A, DONOVAN E, BAGENAL F, et al. Auroral Phenomenology and Magnetospheric Processes: Earth and Other Planets. Washington DC: American Geophysical Union, 2012: 39-54
    [8]
    GRODENT D, BONFOND B, YAO Z, et al. Jupiter’s aurora observed with HST during Juno orbits 3 to 7[J]. Journal of Geophysical Research: Space Physics, 2018, 123(5): 3299-3319 doi: 10.1002/2017JA025046
    [9]
    NICHOLS J D, KAMRAN A, MILAN S E. Machine learning analysis of Jupiter’s far‐ultraviolet auroral morphology[J]. Journal of Geophysical Research: Space Physics, 2019, 124(11): 8884-8892 doi: 10.1029/2019JA027120
    [10]
    HE K M, FAN H Q, WU Y X, et al. Momentum contrast for unsupervised visual representation learning[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020
    [11]
    HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016
    [12]
    CHEN X L, FAN H Q, GIRSHICK R, et al. Improved baselines with momentum contrastive learning[OL]. arXiv preprint arXiv: 2003.04297, 2020
    [13]
    MCLACHLAN G J, BASFORD K E. Mixture models: inference and applications to clustering[M]. New York: Marcel Dekker, 1988
    [14]
    TIBSHIRANI R, WALTHER G, HASTIE T. Estimating the number of clusters in a data set via the gap statistic[J]. Journal of the Royal Statistical Society Series B (Statistical Methodology), 2001, 63(2): 411-423 doi: 10.1111/1467-9868.00293
    [15]
    胡泽骏, 韩冰, 连慧芳. 基于广义回归神经网络的行星际/太阳风参数和地磁指数的紫外极光强度建模[J]. 地球物理学报, 2020, 63(5): 1738-1750

    HU Zejun, HAN Bing, LIAN Huifang. Modeling of ultraviolet auroral intensity based on Generalized Regression Neural Network associated with IMF/solar wind and geomagnetic parameters[J]. Chinese Journal of Geophysics, 2020, 63(5): 1738-1750
    [16]
    韩冰, 连慧芳, 胡泽骏. 基于神经网络模型的紫外极光卵边界建模[J]. 中国科学: 技术科学, 2019, 49(5): 531-542 doi: 10.1360/N092018-00227

    HAN Bing, LIAN Huifang, HU Zejun. Modeling of ultraviolet auroral oval boundaries based on neural network technology[J]. Scientia Sinica Technologica, 2019, 49(5): 531-542 doi: 10.1360/N092018-00227
    [17]
    王倩, 孟庆虎, 胡泽骏, 等. 紫外极光图像极光卵提取方法及其评估[J]. 极地研究, 2011, 23(3): 168-177

    WANG Qian, MENG Qinghu, HU Zejun, et al. A method for extracting auroral ovals in UVI images and its evaluation[J]. Chinese Journal of Polar Research, 2011, 23(3): 168-177
    [18]
    SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations. San Diego: ICLR, 2014
    [19]
    BOYKOV Y Y, JOLLY M P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images[C]//Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. Vancouver: IEEE, 2001
    [20]
    王梓涵, 佟继周, 邹自明, 等. 基于U-net的紫外极光观测极光卵形态提取[J]. 空间科学学报, 2021, 41(4): 667-675 doi: 10.11728/cjss2021.04.667

    WANG Zihan, TONG Jizhou, ZOU Ziming, et al. Auroral oval morphology extraction based on u-net from ultraviolet aurora observation[J]. Chinese Journal of Space Science, 2021, 41(4): 667-675 doi: 10.11728/cjss2021.04.667
    [21]
    VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(11): 2579-2605
    [22]
    RONEN M, FINDER S E, FREIFELD O. DeepDPM: Deep clustering with an unknown number of clusters[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 9851-9860
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(564) PDF Downloads(48) Cited by()
    Proportional views
    Related

    Journal Introduction

    ISSN 0254-6124       CN 11-1783/V

    Copyright © Editorial Office of Chinese Journal of Space Science.  京ICP备08100722号

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    x Close Forever Close

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return