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.

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Validation and Intercomparisons
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Cited articles

Aebi, C., Gröbner, J., and Kämpfer, N.: Cloud fraction determined by thermal infrared and visible all-sky cameras, Atmos. Meas. Tech., 11, 5549–5563, https://doi.org/10.5194/amt-11-5549-2018, 2018. 
Calbo, J. and Sabburg, J.: Feature extraction from whole-sky ground-based images for cloud-type recognition, J. Atmos. Ocean. Tech., 25, 3–14, https://doi.org/10.1175/2007JTECHA959.1, 2008. 
Calbo, J., Long, C. N., Gonzalez, J. A., Augustine, J., and McComiskey, A.: The thin border between cloud and aerosol: Sensitivity of several ground based observation techniques, Atmos. Res., 196, 248–260, https://doi.org/10.1016/j.atm osres.2017.06.010, 2017. 
Cazorla, A., Olmo, F. J., and Alados-Arboledasl, L.: Development of a sky imager for cloud cover assessment, J. Opt. Soc. Am. A., 25, 29–39, https://doi.org/10.1364/JOSAA.25.000029, 2008. 
Cheng, H.-Y. and Lin, C.-L.: Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques, Atmos. Meas. Tech., 10, 199–208, https://doi.org/10.5194/amt-10-199-2017, 2017. 
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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.