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Automatic recognition of poleward moving auroras from all-sky image sequences based on HMM and SVM

机译:基于HMM和SVM的全天空图像序列中极移极光的自动识别

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摘要

We present an automatic method to recognize the poleward moving auroras (PMAs) from all-sky image sequences. A simplified block matching algorithm combined with an orientation coding scheme and histogram statistics strategy was utilized to estimate the auroral motion between interlaced images. An all-sky image sequence was first modeled by hidden Markov models (HMMs) and then represented by HMM similarities. The imbalanced classification problem, i.e., non-PMA events far outnumbering PMA events, was addressed by the metric-driven biased support vector machine (SVM). The proposed method was evaluated using auroral observations in 2003 at the Chinese Yellow River Station. Five days observations were manually labeled as PMA or non-PMA events considering both the keogram and all-sky image information. The supervised classification experiments were carried out and achieved satisfactory results. We further detected PMAs from auroral observations in the remaining days and the resultant double-peak occurrence distribution was compared with that of the well-known poleward moving auroral forms (PMAFs).
机译:我们提出了一种自动方法,可以从全天空图像序列中识别极移运动极光(PMA)。结合方向编码方案和直方图统计策略的简化块匹配算法用于估计隔行图像之间的极光运动。首先通过隐马尔可夫模型(HMM)对全天候图像序列进行建模,然后用HMM相似度表示。度量驱动的有偏支持向量机(SVM)解决了不平衡的分类问题,即非PMA事件远远超过PMA事件。 2003年在中国黄河站利用极光观测对提出的方法进行了评估。五天的观测值被手动标记为PMA或非PMA事件,同时考虑到了心电图和全天候图像信息。进行了监督分类实验,取得了满意的结果。我们在剩余的几天中从极光观测中进一步检测了PMA,并将产生的双峰出现分布与著名的极移极光形式(PMAFs)进行了比较。

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