Cloud Identification and Reconstruction from All-sky Camera Images Based on Star Photometry Estimation

Abstract

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.