  Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a
cost-effective means to understanding cloud cover and weather patterns. The
accurate segmentation of clouds in these images is a challenging task, as
clouds do not possess any clear structure. Several algorithms using different
color models have been proposed in the literature. This paper presents a
systematic approach for the selection of color spaces and components for
optimal segmentation of sky/cloud images. Using mainly principal component
analysis (PCA) and fuzzy clustering for evaluation, we identify the most
suitable color components for this task.

Computer Vision and Pattern Recognition