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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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Chaojun Shi on behalf of the Authors (03 Aug 2019)  Author's response    Manuscript
ED: Publish subject to technical corrections (15 Aug 2019) by Murray Hamilton
AR by Chaojun Shi on behalf of the Authors (19 Aug 2019)  Author's response    Manuscript
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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.