太阳黑子半影纤维亮点的识别以及特征分析

发布时间：2018-03-05 07:24

  本文选题：半影纤维　切入点：形态学重构　出处：《昆明理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：数字图像处理(Digital Image Processing)是将图片信息转换成数字信息然后利用计算机对其进行处理的过程。主要是利用计算机对数字图像进行噪声去除、图像复原、图像分割以及特征提取等,用来改善原始图像的质量,使得原始图像更加清晰,从而得以保存和研究的目的。目前,随着技术的不断发展,数字图像处理也向着更高,更深层方向发展。为天文学领域的发展提供了重要的技术基础。太阳每时每刻都发生着剧烈的变化,在光球层中黑子的运动是太阳活动中典型的活动之一。而黑子是由本影与半影构成,其中半影中包含了亮纤维和暗纤维。研究表明,黑子中有巨大的磁场存在,同样半影上方也有磁场。并且半影之所以呈现纤维状也是与磁场的存在有关。同时,研究发现半影纤维与埃Wr谢德流的形成以及热等离子体的流动有密切的联系。所以为了更好的研究半影纤维形成与磁场的关系,以及埃Wr谢德流的形成等首先需要识别并研究半影纤维的特性。因此,识别半影纤维以及研究其相关特性是非常重要的。通过观察发现,亮纤维是呈彗星状,即靠近本影方向有个局部较亮的点,后面呈丝状。所以,根据亮纤维的形态特征我们通过识别亮纤维中局部较亮的点,并且分析其相关特性来代表半影亮纤维的相关特性。目前,大多数识别半影纤维亮点的方法是基于空域平滑的方法,其本质是通过选用合适的阈值来提取半影纤维亮点,但是阈值的选取是比较繁杂的过程,需要人为的不断调试。同时,其结果的好坏也是由人眼观测来判断,因此存在一定的主观性。所以,为了解决以上问题,本文提出了新的识别方法,运用形态学重构技术识别半影纤维亮点。具体识别过程如下:首先,使用线性滤波技术处理图片降低图像的噪声;然后,提取半影区域。提取半影区域首先需要提取本影区域和黑子与米粒边界,然后才能获得半影区域;最后,在半影区域中通过形态学重构技术重构图片,并得到原图与重构图的差值图,并经过图像归一化,二值化等形态学操作提取半影纤维亮点。本文为了更好的展示识别过程,选用了美国大熊湖太阳天文台1.6米太阳望远镜(New Solar Telescope,简称NST)序列图中第一帧图像作为代表进行了展示。同时,为了验证所提方法的可行性和普适性,本文作了两组对比试验,第一组是通过运用本文所提方法来识别云南天文台抚仙湖真空望远镜(New Vaccum Sloar Telescope,简称 NVST)和太阳能光学望远镜(Solar Optical Telescope,简称 SOT)的数据,从整体上验证方法的普适性。第二组是通过模拟已有方法来识别得到结果图,并与本文识别结果图作对比进而证明方法的可行性。并且,为了评价本文所提算法,我们通过主观评价和客观评价两方面展开了阐述。首先主观评价,本文从三种数据(分别是:SOT,NVST,NST)中随机抽选5帧图像,并用本文所提算法得到识别结果图。采用人工标记的方法分别统计了应该有的半影纤维亮点的个数,实际统计出的个数以及识别出的亮点中正确的个数和误识别的个数。进而得到正确率和误识别率,从这两方面进行主观评价。客观评价,为了和已有文献中的统计结果作对比,本文运用所提算法识别SOT的序列图(共计764张图像),然后统计了识别出的半影纤维亮点的面积大小以及强度大小的相关特性,并与已有文献中的结果作对比从客观方面说明方法的正确性。
[Abstract]:Digital image processing (Digital Image Processing) is the process of image information into digital information and then processed by computer. The main computer is used for noise removal and image restoration of digital image, image segmentation and feature extraction, used to improve the quality of the original image, the original image is more clear, in order to save and the purpose of the study. At present, with the continuous development of technology, digital image processing is more and more deep direction. Provides an important technical basis for the development of the field of astronomy. Every time the sun is undergoing dramatic changes, sunspots in the photosphere in motion is one of the typical activities and sunspots in solar activities. It is composed of umbra and penumbra, which contains a light fiber and penumbra dark fiber. The results show that there exist huge magnetic field in the same spot, on the penumbra We also have the field. And the penumbra showing fibrous and magnetic field exist. At the same time, the study found that formation is associated with Ethiopia Wr schede penumbra fiber flow and thermal plasma flow. So in order to study the fiber formation and better penumbral magnetic field, and the formation of Wr. At the first need to flow the identification and study of the penumbral filaments characteristics. Therefore, the identification and characterization of penumbra fiber is very important. Through the observation that the light fiber is a comet, which is near to the direction of a local umbra lighter, behind the filamentous. So, according to the morphological characteristics of light fiber we identified by local light fiber a bright point, related characteristics and analyze the relevant characteristics to represent the penumbra bright fiber. At present, most methods for discovering penumbral filaments highlight is based on the spatial smoothing method. Nature is by selecting appropriate threshold to extract the penumbra fiber highlights, but the threshold is more complicated, the need of human constantly debugging. At the same time, the result is judged by the human eye observation, so there is certain subjectivity. Therefore, in order to solve the above problems, this paper puts forward a new recognition method, using the morphological identification of reconstruction of penumbral filaments highlights. The specific identification process is as follows: firstly, using the linear filtering image reduce the image noise; then, extracting the penumbra region. The extraction of penumbra area necessary to extract the umbra region and the sunspot and grain boundary, and then to get the penumbra region; finally, through the reconstruction of morphological reconstruction technology of pictures in the penumbra region, and get the difference maps and reconstruction image, and through image normalization, binarization and morphological operations to extract the semi Ying fiber highlights. In order to better show the recognition process, the 1.6 meter solar telescope in Big Bear Lake Solar Observatory (New Solar Telescope, referred to as NST) the first frame of the image sequence diagram as a representative of the show. At the same time, in order to verify the applicability and feasibility of the proposed method in this paper p, two groups of contrast experiments, the first group is through the use of the identification of Fuxian Lake vacuum telescope of Yunnan Observatory, the proposed method (New Vaccum Sloar Telescope, referred to as NVST) and solar telescope (Solar Optical Telescope, referred to as SOT) data from the whole verification method is universal. The second group is simulated by existing methods to get recognition results, and the feasibility and the identification results are compared and proved diagram method. And, in order to evaluate the proposed algorithm, we through the two aspects of subjective evaluation and objective evaluation. Firstly, elaborates the main 瑙傝瘎浠，

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