论文标题
在太阳图像和太阳预测图分类中自动分割冠状孔
Automatic Segmentation of Coronal Holes in Solar Images and Solar Prediction Map Classification
论文作者
论文摘要
太阳图像分析依赖于检测冠状孔来预测地球磁场的破坏。冠状孔充当了可以到达地球的太阳风的来源。因此，在物理模型中使用冠状孔来预测太阳风的演变及其干扰地球磁场的潜力。由于物理模型中固有的不确定性，因此需要一个分类系统，该分类系统可用于选择最适合观察到的冠状孔的物理模型。 物理模型分类问题分解为三个子问题。首先，他论文开发了一种冠状孔分割的方法。其次，论文开发了与不同地图匹配冠状孔的方法。第三，根据匹配结果，论文开发了物理地图分类系统。 一种水平分割方法用于检测在极端超紫色图像（EUVI）和磁场图像中观察到的冠状孔。为了验证分割方法，将两个独立的手动分割组合在一起，以产生46个共识图。总体而言，级别分割方法比当前方法产生了重大改进。 物理图分类基于物理图之间的冠状孔匹配，（i）共识图（半自动化）或（ii）分段地图（完全自动化）。根据匹配结果，系统使用区域差异，匹配的群集，新和缺失的冠状孔簇之间的最短距离来对每个地图进行分类。结果表明，自动分割和分类系统的性能要比单个人类更好。
Solar image analysis relies on the detection of coronal holes for predicting disruptions to earth's magnetic field. The coronal holes act as sources of solar wind that can reach the earth. Thus, coronal holes are used in physical models for predicting the evolution of solar wind and its potential for interfering with the earth's magnetic field. Due to inherent uncertainties in the physical models, there is a need for a classification system that can be used to select the physical models that best match the observed coronal holes. The physical model classification problem is decomposed into three subproblems. First, he thesis develops a method for coronal hole segmentation. Second, the thesis develops methods for matching coronal holes from different maps. Third, based on the matching results, the thesis develops a physical map classification system. A level-set segmentation method is used for detecting coronal holes that are observed in extreme ultra-violet images (EUVI) and magnetic field images. For validating the segmentation approach, two independent manual segmentations were combined to produce 46 consensus maps. Overall, the level-set segmentation approach produces significant improvements over current approaches. Physical map classification is based on coronal hole matching between the physical maps and (i) the consensus maps (semi-automated), or (ii) the segmented maps (fully-automated). Based on the matching results, the system uses area differences,shortest distances between matched clusters, number and areas of new and missing coronal hole clusters to classify each map. The results indicate that the automated segmentation and classification system performs better than individual humans.
