Cloud segmentation in astronomical images using gray level co-occurrence matrix and mini-batch <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg"> <mml:mi>k</mml:mi> </mml:math> -means

Night-time astronomical observations, which rely on ground-based optical telescopes with large field of view, are significantly affected by the clouds. Valuable information is not found solely in absolutely cloud-free sky regions, especially when tracking celestial targets. But cloud-affected images are typically discarded or otherwise poorly utilized, resulting in substantial waste of observational resources. To address this issue, a method of image segmentation is proposed as a means of extracting useful information from images affected by clouds. An objective image is divided into a number of regions, each exhibiting a different level of cloud interference, in order to extract valuable information. The gray level co-occurrence matrix is applied to the smoothed images, and the generated texture features are clustered combining mini-batch k -means with 8-connected component to classify regions into three segments: thick cloud, thin cloud and cloud-free ones. The efficacy of the proposed cloud segmentation is demonstrated by examining the magnitude-flux formulae of the sources. Furthermore, the robustness of the method is illustrated by statistical analysis of the O - C of the magnitudes in the presence of various perturbations.