Cloud cover significantly influences ground-based optical astronomical observations, with nighttime astronomy often relying on visible light all-sky cameras for cloud detection. However, existing algorithms for processing all-sky cloud images typically require extensive manual intervention, posing challenges in identifying clouds with pronounced extinction characteristics. Furthermore, there is a lack of effective means for detailed visualization of cloud cover. To address these issues, this paper proposes a method that reconstructs the cloud distribution and thickness from all-sky images through star identification and photometry. Specifically, a high-precision star coordinate to the pixel position imaging model calibration method based on the star recognition for fisheye lenses is investigated, resulting in an all-sky rms error of less than 0.87 pixels. Based on the comprehensive reference star catalog, an optimized star extraction method based on SExtractor is developed to handle the difficulty of image source detection in all-sky cloud images. The optical thickness and distribution of cloud layers is calculated through star matching and extinction measurements. Finally, contingent upon the capability of camera and catalog star density, seven cloud layer reconstruction methods are proposed based on meshing and machine learning techniques, achieving a reconstruction accuracy of up to 1.°8. The processing results from real observed images indicate that the proposed method offers a straightforward calibration process and delivers excellent cloud cover extraction and reconstruction outcomes, thereby providing practical value in telescope dynamic scheduling, site characterization and the development of observation strategies.

中文翻译：

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基于星光测量估计的全天摄像机图像云识别与重建

云量显着影响地面光学天文观测，夜间天文学通常依靠可见光全天摄像机进行云检测。然而，现有的处理全天空云图像的算法通常需要大量的人工干预，这给识别具有明显消光特征的云带来了挑战。此外，缺乏对云量进行详细可视化的有效手段。为了解决这些问题，本文提出了一种通过恒星识别和光度测量从全天空图像中重建云分布和厚度的方法。具体来说，研究了一种基于鱼眼镜头星识别的高精度星坐标到像素位置成像模型标定方法，全天均方根误差小于0.87像素。基于综合参考星表，提出了一种优化的恒星提取方法性提取器是为了解决全天云图像中图像源检测的难题而开发的。通过恒星匹配和消光测量来计算云层的光学厚度和分布。最后，根据相机和星表密度的能力，基于网格划分和机器学习技术，提出了七种云层重建方法，重建精度高达1.°8。真实观测图像的处理结果表明，该方法提供了简单的校准过程，并提供了出色的云量提取和重建结果，从而为望远镜动态调度、站点表征和观测策略的制定提供了实用价值。