Cloud Region Segmentation from All Sky Images using Double K-Means Clustering

The segmentation of sky images into regions of cloud and clear sky allows atmospheric scientists to determine the fraction of cloud cover and the distribution of cloud without resorting to subjective estimates by a human observer. This is a challenging problem because cloud boundaries and cirroform cloud regions are often semi-transparent and indistinct. In this study, we propose a lightweight, unsupervised methodology to identify cloud regions in ground-based hemispherical sky images. Our method offers a fast and adaptive approach without the necessity of fixed thresholds by utilizing K-means clustering on transformed pixel values. We present the results of our method for two data sets and compare them with three different methods in the literature.