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太阳光球磁亮点的识别算法
Alternative TitleA Region-Growth Algorithm to Recognize Magnetic Bright Spots in the Solar Photosphere
刘艳霄1,2; 杨云飞3; 林隽1
Source Publication天文研究与技术(Astronomical Research & Technology)
2014-04
Volume11Issue:2Pages:145-150
DOI10.14005/j.cnki.issn1672-7673.2014.02.015
ClassificationP182.2+1
Contribution Rank第1完成单位
Indexed ByCSCD
Keyword太阳光球磁亮点 图像识别 拉普拉斯算子 图像分割 区域生长法
Abstract

区域生长法是一种基于区域分割的算法,其关键在于种子点的准确提取和生长准则的定义。用区域生长法对云南天文台澄江1 m红外太阳塔望远镜(New Vacuum Solar Telescope,NVST)在TiO(705.8nm)波段的观测资料进行分析识别,采用拉普拉斯算子提取种子点,然后用图像灰度阈值作为生长准则对种子点进行生长,最后剔除误识别的米粒,从而完成对磁亮点的识别工作。然后又对Hinode的观测资料进行了识别并与Utz等人的结果进行对比。

Other Abstract

Magnetic bright spots are the smallest magnetic structures in the solar photosphere. They are located in lanes between solar granules. Their sizes are about 100km to 300km, and their lifetimes range from several seconds to tens of minutes. It is important for solar physics to extensively study magnetic bright spots. For example,magnetic bright spots are considered as tracers of active regions whose flux ropes stretch into the solar corona. Motions of magnetic bright spots may have important impact on the heating of the solar chromosphere and corona. In addition,studies of magnetic bright spots can improve our knowledge about the solar sub-photosphere. Accurate recognitions of magnetic bright spots serve as the basis for all relevant important studies. The region-growth algorithm for recognizing magnetic bright spots is based on the image segmentation technique. The key steps of the algorithm are to select the seeds for the region growth and to define growth rules. In this paper we use certain data observed at the TiO wavelength by the 1m new vacuum solar telescope of the Yunnan Observatories. In applying the algorithm,we extract seeds as certain pixels in the convolution of a data image using a Laplacian mask. The pixels selected as seeds have post-convolution values passing a threshold. Our growth rule is that a pixel is included in a region for a spot if the gray value there passes a threshold. After processing with the algorithm we remove features falsely selected by the algorithm. We also apply the algorithm to some G-band data observed by the Solar Optical Telescope on the Hinode. We compare our results to those of Utz et al. We find that diameters of magnetic bright spots have an average 166. 2km, which is consistent with the average given by Utz et al. 166km. This supports the reliability of our recognition approach.

Funding Project国家自然科学基金[11273055] ; 国家自然科学基金[11333007] ; 国家重点基础研究发展计划[973计划][2011CB811403] ; 国家重点基础研究发展计划(973计划)[2013CBA01503] ; 科学院知识创新方向性项目[KJCX2-EW-707] ; 先导专项B类项目[XDB09000000]
Funding Organization国家自然科学基金[11273055, 11333007] ; 国家重点基础研究发展计划[973计划][2011CB811403, 2013CBA01503] ; 科学院知识创新方向性项目[KJCX2-EW-707] ; 先导专项B类项目[XDB09000000]
Language中文
Subject Area天文学 ; 太阳与太阳系 ; 太阳物理学
MOST Discipline Catalogue理学 ; 理学::天文学
ISSN1672-7673
Archive Date2014-03-24
CSCD IDCSCD:5112460
Citation statistics
Cited Times:4[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.ynao.ac.cn/handle/114a53/6502
Collection太阳物理研究组
Affiliation1.中国科学院云南天文台, 云南, 昆明, 650011
2.中国科学院大学, 北京, 100049
3.昆明理工大学计算机应用重点实验室和信息工程与自动化学院, 云南, 昆明, 650500
First Author AffilicationYunnan Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
刘艳霄,杨云飞,林隽. 太阳光球磁亮点的识别算法[J]. 天文研究与技术(Astronomical Research & Technology),2014,11(2):145-150.
APA 刘艳霄,杨云飞,&林隽.(2014).太阳光球磁亮点的识别算法.天文研究与技术(Astronomical Research & Technology),11(2),145-150.
MLA 刘艳霄,et al."太阳光球磁亮点的识别算法".天文研究与技术(Astronomical Research & Technology) 11.2(2014):145-150.
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