NSSC OpenIR  > 空间环境部
Alternative TitleDetection for coronal mass ejection based on Gaussian Mixture Models
曾丹丹; 白先勇; 强振平; 李强; 季凯帆
Source Publication科学通报
Keyword混合高斯模型 日冕物质抛射 探测 背景差分
Abstract提出了一种基于混合高斯模型的日冕物质抛射(CME)探测方法. 基本思想是利用自适应的混合高斯模型建立较为稳定的日心极坐标下的日冕图像的动态背景, 从而探测作为前景变化的CME. 采用SOHO卫星上的大视角分光日冕仪(LASCO)观测的2组日冕序列图像作为研究对象, 研究的内容主要包括日冕序列图像的预处理、CME的探测、自适应混合高斯背景差分法与其他多种CME探测方法的对比3个方面. 实验结果表明, 自适应混合高斯背景差分法探测CME是可行的, 它能探测到CDAW手动目录列出的全部CME, 还能探测到CDAW探测不到的强度弱和张角小的CME, 而且探测数量也多于CACTus和SEEDS探测算法.
Other AbstractA new automatic detection algorithm extracted solar moving target coronal mass ejection (CME) is proposed in this paper. The CME releases huge quantities of matter and electromagnetic radiation solar-terrestrial space from the Sun. When the ejection is directed toward the Earth, it even effects the life of human being. Automatic detecting the CME for observed image is very useful for studying those solar activities. The CME detection can be considered as detection and tracing moving object in a complicated background. Therefore, according to the gradient characteristic of the background and the demand of real-time processing, we developed a dynamic new background estimation algorithm that based on the Adaptive traditional Gaussian Mixture Model. An expectation-maximization algorithm is applied to improve the initialization of the model, and a learning rate for each pixel in the sequence of image is adaptively for updating the background of coronal sequential images. The CME can be detected as a foreground object after subtracting the background that is estimated by the adaptive Mixture Gaussian Models from the original image extraction. The processing is under the polar coordinate of heliocentric. Two image sequences of coronal observed by the Large Angle Spectroscopic Coronagraph (LASCO) in the SOHO satellite were used. The paper gives the details of the preprocessing of the image sequences of coronal, detecting of the CME and the results comparison between the proposed and other CME automatic detection methods. The detection rate, false alarm rate, and detection of the amount of the CME are discussed. The experimental results show that the method is practicable and effective for detecting the CME. Compared with the manual detection of moving target detection, such as CDAW, the automatic CME detection method is more rapid and powerful. The method not only can detect all of the CMEs listed on the CDAW CME catalog, but also the CME with weaker intensity and smaller angle. Furthermore, it has better performance than the algorithm of the CACTus and the SEEDS. The method with adaptive mixture Gaussian background subtraction has higher detection rate, lower false alarm rate and more effectively for detecting the CME than the SEEDS and CACTus, but it takes more computer time and needs more computational capabilities. Our model has a preferable adaptive in the case of uncertain factors, and correspondence quickly.
Indexed ByEI ; CSCD
Citation statistics
Document Type期刊论文
Recommended Citation
GB/T 7714
曾丹丹,白先勇,强振平,等. 基于混合高斯模型的日冕物质抛射探测方法[J]. 科学通报,2016,61(11):1255-1264.
APA 曾丹丹,白先勇,强振平,李强,&季凯帆.(2016).基于混合高斯模型的日冕物质抛射探测方法.科学通报,61(11),1255-1264.
MLA 曾丹丹,et al."基于混合高斯模型的日冕物质抛射探测方法".科学通报 61.11(2016):1255-1264.
Files in This Item: Download All
File Name/Size DocType Version Access License
201661111255-1264.pd(3490KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[曾丹丹]'s Articles
[白先勇]'s Articles
[强振平]'s Articles
Baidu academic
Similar articles in Baidu academic
[曾丹丹]'s Articles
[白先勇]'s Articles
[强振平]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[曾丹丹]'s Articles
[白先勇]'s Articles
[强振平]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 201661111255-1264.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.