Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
This work addresses the need for reliable cloud segmentation in fields like climate modeling and renewable energy, offering an incremental improvement with a new dataset.
The paper tackles the challenge of accurately segmenting clouds in ground-based sky images by proposing a supervised framework using partial least squares regression and analyzing color spaces, achieving robust segmentation without manually-defined parameters. It also releases the SWIMSEG database of annotated images to support research.
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.