Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks
<p>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. This paper proposes a new automatic cloud segmentation algorithm that utilizes the advantages of deep-learning fully...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-09-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/12/4713/2019/amt-12-4713-2019.pdf |
| Summary: | <p>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. This paper proposes a
new automatic cloud segmentation algorithm that utilizes the advantages of
deep-learning fully convolutional networks (FCNs) to segment cloud pixels
from diurnal and nocturnal ASI images; it is called the enhancement fully
convolutional network (EFCN). Firstly, all the ASI images in the data set
from the Key Laboratory of Optical Astronomy at the National Astronomical
Observatories of Chinese Academy of Sciences (CAS) are converted from the
red–green–blue (RGB) color space to hue saturation intensity (HSI) color
space. Secondly, the I channel of the HSI color space is enhanced by
histogram equalization. Thirdly, all the ASI images are converted from
the HSI color space to RGB color space. Then after 100 000 iterative
trainings based on the ASI images in the training set, the optimum associated
parameters of the EFCN-8s model are obtained. Finally, we use the trained
EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the
experiments our proposed EFCN-8s was compared with four other algorithms
(OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics.
Experiments show that the EFCN-8s is much more accurate in cloud
segmentation for diurnal and nocturnal ASI images than the other four
algorithms.</p> |
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| ISSN: | 1867-1381 1867-8548 |