We describe the application of semantic segmentation by using the self-organizing map technique to an high spatial and spectral resolution data set acquired along the H α line at 656.28 nm by the Interferometric Bi-dimensional Spectrometer installed at the focus plane of the Dunn solar telescope. This machine learning approach allowed us to identify several features corresponding to the main structures of the solar photosphere and chromosphere. The obtained results show the capability and flexibility of this method to identifying and analysing the fine structures which characterize the solar activity in the low atmosphere. This is a first successful application of the SOM technique to astrophysical data sets.

中文翻译：

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基于自组织映射技术的低太阳大气光谱图像分割

我们通过安装在 Dunn 太阳望远镜焦平面上的干涉二维光谱仪在 656.28 nm 处沿 H α 线采集的高空间和光谱分辨率数据集描述了语义分割的应用。 . 这种机器学习方法使我们能够识别与太阳光球和色球的主要结构相对应的几个特征。所得结果表明该方法在识别和分析表征低层大气中太阳活动的精细结构方面的能力和灵活性。这是 SOM 技术首次成功应用于天体物理数据集。