可处理暗腔的日冕物质抛射检测新方法

许美慧; 尹建芹; 张玲; 姚海; 于峻伟

doi:10.3788/OPE.20162413.0591

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可处理暗腔的日冕物质抛射检测新方法

更新时间：2020-08-13

- 可处理暗腔的日冕物质抛射检测新方法
- New detection method for coronal mass ejection capable of dark cavity processing
- 光学精密工程 2016年24卷第10s期页码：591-599
- 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：
  
  国家自然科学基金资助项目（No.61203341，No.61375084）();济南大学自然基金资助项目（No.XKY1513）();济南大学博士基金资助项目（No.1
- DOI：10.3788/OPE.20162413.0591
  中图分类号：
- 收稿日期：2016-06-05，
  
  修回日期：2016-06-12，
  
  纸质出版日期：2016-11-14
- 稿件说明：
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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

references

REVATHY K, LEKSHMI S, NAYAR S R P. Fractal-based fuzzy technique for detection of active regions from solar images[J]. Solar Physics, 2005, 228(1-2):43-53.

JACOBS M, CHANG L, PULKKINEN A. Automatic segmentation and classification of multiple coronal mass ejections from coronagraph images[C].Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2013:1.

ATTRILL G D R. Low coronal signatures of coronal mass ejections:coronal "waves" and dimmings[D]. London:University College London, 2009.

SCHUH M A, BANDA J M, WYLIE T, et al.. On visualization techniques for solar data mining[J]. Astronomy and Computing, 2015, 10(16):32-42.

LIANG Y, GAO Y. A median filtering algorithm based on selected point in digital image[C]. 2013 International Conference on Information Science and Cloud Computing Companion (ISCC-C), 2013:757-762.

RAJESWARI K, ACHARYA O, SHARMA M, et al.. Improvement in k-means clustering algorithm using data clustering[C]. 2015 International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE, 2015:367-369.

BAO Y, QI B, GU F. Facial recognition using partial Log-Polar transformation[C]. 2011 IEEE/SICE International Symposium on System Integration (SⅡ) IEEE, 2011:74-77.

CRACIUNESCU A, CIUMBULEA G S, DUMITRICA C N. Space phasor geometrical loci in polar coordinates as voltage unbalance monitoring tool[C]. 20138th International Symposium on Advanced Topics in Electrical Engineering (ATEE), IEEE, 2013:1-4.

BANDA J M, ANGRYK R A. On the effectiveness of fuzzy clustering as a data discretization technique for large-scale classification of solar images[C]. IEEE International Conference on Fuzzy Systems IEEE, 2009:2019-2024.

JACOBS M, PULKKINEN A, CHANG L. Improving coronal mass ejection segmentation using pattern recognition techniques[C].Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2012:1.

曾昭宪,刘毅,魏雅利. 基于频谱突变分析的日冕物质抛射识别方法[J]. 华中科技大学学报(自然科学版), 2012, 40(51):379-383. ZENG ZH X,LIU Y,WEI Y L. Detecting coronal mass ejection method by analyzing spectrum change[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition),2012,40(51):379-383.(in Chinese)

BERGHMANS D, FOING B H, FLECK B. Automated detection of CMEs in LASCO data[C]. In Proceedings of The SOHO-11 Symposium on From Solar Min to Max:Half a Solar Cycle with SOHO, 2002,437-440.

KAUR J, SINGH H. Performance evaluation of a novel hybrid clustering algorithm using birch and K-means[C].2015 Annual IEEE India Conference (INDICON), 2015:1-6.

张坤华, 杨烜. 应用聚类和分形实现复杂背景下的扩展目标分割[J]. 光学精密工程, 2009, 17(7):1665-1671. ZHANG K H, YANG X. Segmentation for extended target in complex backgrounds based on clustering and fractal[J]. Opt. Precision Eng.,2009, 17(7):1665-1671. (in Chinese)

张建伟, 张启衡. 基于块遍历的直线边缘特征提取[J]. 光学精密工程, 2009, 17(3):662-668. ZHANG J W, ZHANG Q H. Line-edge feature extraction based on ergodic block[J]. Opt. Precision Eng.,2009, 17(3):662-668. (in Chinese)

张山,谭久彬,王雷, 等. 直角坐标激光直写的动态曝光模型[J]. 光学精密工程, 2008, 16(8):1354-1360. ZHANG SH, TAN J L, WANG L, et al.. Dynamic exposure model of laser direct writing in Cartesian coordinate[J]. Opt. Precision Eng., 2008, 16(8):1354-1360. (in Chinese)

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