Clustering of Ultraviolet Auroral Oval Images Based on Deep Representation Learning
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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。
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关键词:
- 极光卵及形态归类 /
- 紫外极光卵图像聚类 /
- MoCo-GMM模型
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. -
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 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
Algorithm Result Representation degree ORB No unified dimensions \ LBP No unified dimensions \ PCA 15 dimensions 0.865 HOG 3600 dimensions 0.854 MoCo 512 dimensions 0.892 注 字体加黑组表示模型效果更优。 
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表 2 各特征聚类算法效果对比
Table 2. Comparison of six feature clustering algorithms
Model clusters CH SC GMM 10 183.14 0.057 K-means++ 10 182.79 0.054 DBSCAN 5 132.77 –0.028 11 67.06 –0.031 Spectral clustering 10 152.37 0.044 BIRCH 10 151.63 0.035 AHC 10 151.05 0.034 注 字体加黑组表示模型效果更优。
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