太阳光球磁亮点的识别算法
A Region-Growth Algorithm to Recognize Magnetic Bright Spots in the Solar Photosphere
查看参考文献13篇
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文摘
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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Ti0(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。 |
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其他语种文摘
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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. |
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来源
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天文研究与技术
,2014,11(2):145-150 【扩展库】
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关键词
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太阳光球磁亮点
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图像识别
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拉普拉斯算子
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图像分割
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区域生长法
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地址
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1.
中国科学院云南天文台, 云南, 昆明, 650011
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昆明理工大学信息工程与自动化学院, 云南省计算机应用重点实验室, 云南, 昆明, 650500
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1672-7673 |
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学科
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天文学 |
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基金
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国家自然科学基金
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国家重点基础研究发展计划(973计划)
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中国科学院知识创新工程重要方向项目
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先导专项B类项目
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文献收藏号
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CSCD:5112460
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参考文献 共
13
共1页
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