Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23
Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23
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DOI：
10.1051/0004-6361/200811416
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发表时间：
2009-10
影响因子：
6.5
通讯作者：
V. Barra;V. Delouille;M. Kretzschmar;J. Hochedez
中科院分区：
文献类型：
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作者：
V. Barra;V. Delouille;M. Kretzschmar;J. Hochedez
上下文研究日冕的可变性和监测冕洞、平静太阳和活动区，在天体物理学以及空间天气和空间气候应用中具有重要意义。目标。在以前的工作中，我们提出了空间可能性聚类算法（SPoCA）。这是一种多通道无监督空间约束模糊聚类方法，自动分割太阳极紫外（EUV）图像到感兴趣的区域。我们报告的结果，从1997年2月至2005年5月拍摄的SoHO-EIT图像与以前的知识在面积和强度估计是一致的。然而，由于方法本身，它们呈现出一些伪影。方法.在此，我们提出了一种新的算法，基于SPoCA，消除这些文物。我们重点关注两点：关于感兴趣区域的最优聚类的定义以及聚类边缘的准确定义。此外，我们提出了这种方法的方法扩展，我们说明这些扩展与自动跟踪的活动区域。结果改进后的算法可以将第23太阳活动周的EIT太阳图像分解为宁静太阳、冕洞和活动区。从分割，即面积，平均强度和相对贡献的太阳辐照度的参数的变化，是与以前的结果一致，从而验证了分解。此外，我们发现每个区域的平均强度与太阳活动周期相关的小变化的迹象。结论.该方法足够通用，允许引入其他通道或数据。现在预计会有新的应用，例如与空间数据观测组织-航空影响评估数据有关的应用。
Context. The study of the variability of the solar corona and the monitoring of coronal holes, quiet sun and active regions are of great importance in astrophysics as well as for space weather and space climate applications. Aims. In a previous work, we presented the spatial possibilistic clustering algorithm (SPoCA). This is a multi-channel unsupervised spatially-constrained fuzzy clustering method that automatically segments solar extreme ultraviolet (EUV) images into regions of interest. The results we reported on SoHO-EIT images taken from February 1997 to May 2005 were consistent with previous knowledge in terms of both areas and intensity estimations. However, they presented some artifacts due to the method itself. Methods. Herein, we propose a new algorithm, based on SPoCA, that removes these artifacts. We focus on two points: the definition of an optimal clustering with respect to the regions of interest, and the accurate definition of the cluster edges. We moreover propose methodological extensions to this method, and we illustrate these extensions with the automatic tracking of active regions. Results. The much improved algorithm can decompose the whole set of EIT solar images over the 23rd solar cycle into regions that can clearly be identified as quiet sun, coronal hole and active region. The variations of the parameters resulting from the segmentation, i.e. the area, mean intensity, and relative contribution to the solar irradiance, are consistent with previous results and thus validate the decomposition. Furthermore, we find indications for a small variation of the mean intensity of each region in correlation with the solar cycle. Conclusions. The method is generic enough to allow the introduction of other channels or data. New applications are now expected, e.g. related to SDO-AIA data.