  Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers)
are increasingly used nowadays because of their applications in a number of
fields, including climate modeling, weather prediction, renewable energy
generation, and satellite communications. Due to the wide variety of cloud
types and lighting conditions in such images, accurate and robust segmentation
of clouds is challenging. In this paper, we present a supervised segmentation
framework for ground-based sky/cloud images based on a systematic analysis of
different color spaces and components, using partial least squares (PLS)
regression. Unlike other state-of-the-art methods, our proposed approach is
entirely learning-based and does not require any manually-defined parameters.
In addition, we release the Singapore Whole Sky IMaging SEGmentation Database
(SWIMSEG), a large database of annotated sky/cloud images, to the research
community.
