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基于深度表征学习的紫外极光卵图像聚类

张龄舒 邹自明 白曦

张龄舒, 邹自明, 白曦. 基于深度表征学习的紫外极光卵图像聚类[J]. 空间科学学报, 2023, 43(2): 219-230. doi: 10.11728/cjss2023.02.220127012
引用本文: 张龄舒, 邹自明, 白曦. 基于深度表征学习的紫外极光卵图像聚类[J]. 空间科学学报, 2023, 43(2): 219-230. doi: 10.11728/cjss2023.02.220127012
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

基于深度表征学习的紫外极光卵图像聚类

doi: 10.11728/cjss2023.02.220127012
基金项目: 中国科学院网信专项资助(CAS-WX2021PY-0101)
详细信息
    作者简介:

    张龄舒:E-mail:zhanglingshu19@mails.ucas.edu.cn

    通讯作者:

    邹自明,E-mail:mzou@nssc.ac.cn

  • 中图分类号: P353

Clustering of Ultraviolet Auroral Oval Images Based on Deep Representation Learning

  • 摘要: 极光受太阳风驱动的地磁亚暴等大尺度动力学影响,其形态及演化因不同的太阳风-磁层-电离层耦合作用可能表现不同。目前,极光卵及其形态的归类大多依据极光演化理论作主观定性分析,没有明确的分类标准,故难以借助统计分析方法和有监督分类模型开展客观定量研究。建立了基于深度表征学习的紫外极光卵图像聚类模型(MoCo-GMM),并利用空间环境参数设计了评估模型物理合理性的方法,在大规模POLAR卫星紫外极光卵图像数据上进行了实验,聚类结果不仅具有良好的簇内凝聚性和簇间分散性,且具备一定的物理可解释性,有效实现了基于图像的极光卵及其形态的客观归类。

     

  • 图  1  紫外极光卵图像聚类流程

    Figure  1.  Pipeline of UV auroral oval image clustering

    图  2  MoCo对比学习紫外极光卵图像表征

    Figure  2.  Contrastive learning of UV auroral oval image representations with MoCo

    图  3  VggNet-16迭代式分类的准确率和损失值变化曲线

    Figure  3.  Accuracy and loss curves of iterative classification with VggNet-16

    图  4  不同模型对日晖干扰图像和极光卵边界模糊图像的形态提取掩膜对比

    Figure  4.  Comparison of morphology extraction masks obtained by different models from images with dayglow effect and images with blurred boundary

    图  5  紫外极光卵图像数据预处理管线及效果

    Figure  5.  Pipeline and results of UV auroral oval image preprocess

    图  6  不同编码器结构的MoCo表征学习的准确率变化曲线

    Figure  6.  Accuracy curve of MoCo representation learning with different encoders

    图  7  预估聚类簇数K。 (a) Cost-K曲线,(b)间隔统计量法条状图

    Figure  7.  Estimate K, number of clusters. (a) Cost-K curve. (b) Gap statistic bar graph

    图  8  GMM 10-聚类结果可视化

    Figure  8.  Visualization of GMM 10-clustering results

    图  9  POLAR紫外极光卵图像MoCo-GMM 10-聚类结果

    Figure  9.  MoCo-GMM 10-clustering results of POLAR UV auroral oval images

    图  10  不同模型提取同一幅紫外极光卵图像特征的10-最近邻对应图像

    Figure  10.  UV auroral oval images corresponding to 10-nearest neighbors of feature extracted from the same image with different models

    图  11  不同空间环境参数组合与紫外极光卵图像10-聚类结果的关联度

    Figure  11.  Relation degree between different combinations of space environment parameters and UV auroral oval image 10-clustering results

    表  1  各算法紫外极光卵图像特征提取结果对比

    Table  1.   Comparison of UV auroral oval image feature extraction results with different algorithms

    AlgorithmResultRepresentation degree
    ORBNo unified dimensions\
    LBPNo unified dimensions\
    PCA15 dimensions0.865
    HOG3600 dimensions0.854
    MoCo512 dimensions0.892
     字体加黑组表示模型效果更优。
    下载: 导出CSV

    表  2  各特征聚类算法效果对比

    Table  2.   Comparison of six feature clustering algorithms

    ModelclustersCHSC
    GMM10183.140.057
    K-means++10182.790.054
    DBSCAN5132.77–0.028
    1167.06–0.031
    Spectral clustering10152.370.044
    BIRCH10151.630.035
    AHC10151.050.034
     字体加黑组表示模型效果更优。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-27
  • 录用日期:  2022-05-20
  • 修回日期:  2022-11-07
  • 网络出版日期:  2023-02-13

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