基于先验阈值面的夜空云图分割算法

Night-Sky Cloud Image Segmentation Algorithm Based on Prior Threshold Surface

夜空背景易受到大气污染、月光及黄道带光等影响,导致云图变化较大.传统阈值法仅利用像素点灰度值及相应的邻域信息,难以准确分割背景变化较大且灰度不均匀的云图.文中对大量云图统计分析,发现夜空云图的两个先验特征,提出一种基于先验阈值面的云分割算法.首先根据先验特征分割出可信的背景区域,再根据背景区域进行多项式拟合得到一个随图像区域信息变化的阈值面,阈值面上的各个值介于目标和背景之间,从而可将云从背景分割出来.实验证明,相对于传统的单阈值算法,本文算法分割精确度更高,应用范围更广,尤其对受干扰的云图有更好的分割效果.

Affected by the atmospheric pollution, the moonlight and the zodiac light, the night-sky cloud images vary greatly. The conventional threshold methods which only utilize pixel gray value as well as the neighborhood information are difficult to segment the image accurately because of their uneven backgrounds. In this paper, two prior features of cloud images are observed from the statistical analysis, and a prior threshold surface based cloud segmentation algorithm is presented. After reliable background regions are extracted according to the prior features, an adaptive threshold surface can be obtained by polynomial fitting on the background regions, and the values of the threshold surface are between the clouds and backgrounds. Thus the cloud can be segmented from the background. The experimental results show that the proposed algorithm is more feasible and effective compared with other existing algorithms. Moreover, it produces fine results on the cloud images of light influence.