CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
This work addresses the challenge of accurate cloud segmentation for atmospheric analysis, integrating day and night processing into a single framework, though it is incremental as it builds on existing deep-learning approaches.
The paper tackles the problem of segmenting clouds in both daytime and nighttime sky images, which previously required separate methods, by proposing CloudSegNet, a lightweight deep-learning architecture that achieves state-of-the-art results on public databases.
We analyze clouds in the earth's atmosphere using ground-based sky cameras. An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds. Several techniques have been proposed that use color as the discriminatory feature for cloud detection. In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications. In this paper, we propose a light-weight deep-learning architecture called CloudSegNet. It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework, and achieves state-of-the-art results on public databases.