Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 9
Articles | Volume 12, issue 9
https://doi.org/10.5194/amt-12-4713-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-4713-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 9
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, Yatong Zhou, Bo Qiu, Jingfei He, Mu Ding, and Shiya Wei

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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.
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