Articles | Volume 12, issue 9
Atmos. Meas. Tech., 12, 4713–4724, 2019
https://doi.org/10.5194/amt-12-4713-2019
Atmos. Meas. Tech., 12, 4713–4724, 2019
https://doi.org/10.5194/amt-12-4713-2019
Research article
04 Sep 2019
Research article | 04 Sep 2019

Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

Chaojun Shi et al.

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Latest update: 15 May 2022
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Short summary
Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. We propose a new automatic cloud segmentation algorithm to segment cloud pixels from diurnal and nocturnal ASI images called an enhancement fully convolutional network (EFCN). Experiments showed that the proposed EFCN was much more accurate in cloud segmentation for diurnal and nocturnal ASI images.