New detection method for coronal mass ejection capable of dark cavity processing

XU Mei-hui; YIN Jian-qin; ZHANG Ling; YAO Hai; YU Jun-wei

doi:10.3788/OPE.20162413.0591

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New detection method for coronal mass ejection capable of dark cavity processing

更新时间：2020-08-13

- New detection method for coronal mass ejection capable of dark cavity processing
- Optics and Precision Engineering Vol. 24, Issue 10s, Pages: 591-599(2016)
- 作者机构：
  
  济南大学信息科学与工程学院山东省网络环境智能计算技术重点实验室,山东济南,250022
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162413.0591
  CLC：
- Received：05 June 2016，
  
  Revised：12 June 2016，
  
  Published：14 November 2016
- 稿件说明：
移动端阅览
许美慧, 尹建芹, 张玲等. 可处理暗腔的日冕物质抛射检测新方法[J]. 光学精密工程, 2016,24(10s): 591-599

XU Mei-hui, YIN Jian-qin, ZHANG Ling etc. New detection method for coronal mass ejection capable of dark cavity processing[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 591-599
许美慧, 尹建芹, 张玲等. 可处理暗腔的日冕物质抛射检测新方法[J]. 光学精密工程, 2016,24(10s): 591-599 DOI： 10.3788/OPE.20162413.0591.

XU Mei-hui, YIN Jian-qin, ZHANG Ling etc. New detection method for coronal mass ejection capable of dark cavity processing[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 591-599 DOI： 10.3788/OPE.20162413.0591.

摘要

为了有效识别在大角度光谱日冕仪图像中的日冕物质抛射（Coronal Mass Ejections，CMEs），提出了一种基于

k均值

的可以有效处理暗腔的识别方法。首先采用中值滤波进行预处理去除噪声，然后采用

均值聚类算法对差分图像进行初步分类，统计分类后各类别的分布特性，利用该分布特性识别一级分类，成功识别出明显的高亮CME图像和暗腔CME图像；接着采用二次聚类方法，对于识别成无CME的日冕仪图像采用分块策略和极坐标转换算法，然后对其继续

均值聚类，并对二次聚类结果进行统计，利用分布特性完成二级分类。实验结果表明，该算法能从拍摄的日冕仪图像中较有效地检测出CME图像，并能在图像中直接标明和显示出CME区域点，尤其对带暗腔的CME有较好的效果。对于高亮CME给出的具体测量阈值是第三类类心灰度值为200~250，对于暗腔CME给出的具体测量阈值是第一类类心灰度值在120以下，且类内像素点在1000~30000之间。

Abstract

A k-mean-value based identification method capable of effectively processing the dark cavity was put forward to effectively identify the Coronal Mass Ejections (CMEs) in wide-angle spectrometric coronagraph images. Firstly

median filter was adopted as preprocess algorithm to eliminate the noise

and then difference images was preliminarily classified through the k-mean-value clustering algorithm

distribution characters of each category were classified; The first classification was identified according to the distribution characters

thus the highlighted CME image and dark cavity CME image were successfully identified. Secondly

partitioning strategy and polar coordinate transformation were carried out in the coronagraph image identified with no CME through twice clustering method

and the k-mean-value clustering algorithm was applied in above image

statistics of the twice clustering results was made

and the secondary classification by virtue of the distribution characters was completed. The results show that the method can effectively detect CME image in the coronagraph image

and can directly indicate and display the CME regional point

especially for thoes with dark cavity. The given measurement threshold based on highlighted CME is 200~250 for the center of third classification

while that based on dark cavity CME is lower than 120 for the center of the first classification

and the pixel point within the classification is between 1 000 and 30 000.

关键词

Keywords

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