For auto mated interpretation of star sky images of low luminance and uneven contrast

it is necessary to ensure that the images are not corrupted with clouds. In this paper

we evaluate the problem of low precision and low accuracy of cloud inspection and cloud amount estimation. It is found that there is a low probability of cloud appearance around bright stars as well as in dense fields of stars following statistical analysis of a large number of star sky samples. An adaptive threshold segmentation model of the cloud is established based on a-priori knowledge after analysis of the priori probability. The thresholds applied to different images are adaptively tuned in the present model according to the local backgrounds of stars in an image. By randomly extracting one month period of star images and analyzing their backgrounds

it is verified that the variation of the adaptive thresholds are in accordance with the tendency of a sequence of images in which the gray value of the entire image background changes. Experimental results show that the accuracy of the proposed algorithm has reached up to ninety-five percent or more

a great improvement compared to the traditional algorithm. The proposed algorithm has also been put into practical use.