云的自适应阈值检测方法

Adaptive threshold approach for night-sky cloud inspection

为确保夜间自动拍摄的星空观测图像不受云污染,须对低亮度和对比度不均的夜空图像进行检测。考虑现有云检测不准和检测精度低等问题,文中对大量样本统计分析,发现星体邻域出现云的概率低,密集星体区域出现云的概率也低。根据先验概率建立了一个自适应阈值模型,即不同图像所用阈值由模型根据星云局部背景自动计算调节。通过随机抽取以月为周期的一系列星空图像,分析其天空背景,证明该自适应阈值的变化与整体图像背景灰度的变化趋势相吻合。实验结果表明本文方法对夜空云检测准确度达95%以上,较文中对比的算法有很大提高,并投入实际应用。

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 algo-rithm has also been put into practical use.