Papers › CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
Soumyabrata Dev, Atul Nautiyal, Yee Hui Lee, Stefan Winkler
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.
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