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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Sep 2019
Research article |
| 04 Sep 2019
| 04 Sep 2019
Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks
Chaojun Shi
School of Electronics and Information Engineering, Hebei University of
Technology, Tianjin, 300401, China
Yatong Zhou
CORRESPONDING AUTHOR
School of Electronics and Information Engineering, Hebei University of
Technology, Tianjin, 300401, China
Bo Qiu
School of Electronics and Information Engineering, Hebei University of
Technology, Tianjin, 300401, China
Jingfei He
School of Electronics and Information Engineering, Hebei University of
Technology, Tianjin, 300401, China
Mu Ding
School of Electronics and Information Engineering, Hebei University of
Technology, Tianjin, 300401, China
Shiya Wei
School of Electronics and Information Engineering, Hebei University of
Technology, Tianjin, 300401, China
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Cited
16 citations as recorded by crossref.
- Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset M. Krinitskiy et al. 10.3390/rs15071720
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- CloudRaednet: residual attention-based encoder–decoder network for ground-based cloud images segmentation in nychthemeron C. Shi et al. 10.1080/01431161.2022.2054298
- Explicit basis function kernel methods for cloud segmentation in infrared sky images G. Terrén-Serrano & M. Martínez-Ramón 10.1016/j.egyr.2021.08.020
- Improved RepVGG ground-based cloud image classification with attention convolution C. Shi et al. 10.5194/amt-17-979-2024
- A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism L. Zhang et al. 10.3390/rs14163970
- CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation C. Shi et al. 10.1016/j.energy.2024.133128
- Comparative analysis of methods for cloud segmentation in ground-based infrared images G. Terrén-Serrano & M. Martínez-Ramón 10.1016/j.renene.2021.04.141
- CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning C. Shi et al. 10.1007/s11063-021-10457-2
- Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems G. Terren-Serrano & M. Martinez-Ramon 10.1109/TGRS.2022.3154710
- CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation C. Shi et al. 10.1109/LGRS.2020.3009227
- Processing of global solar irradiance and ground-based infrared sky images for solar nowcasting and intra-hour forecasting applications G. Terrén-Serrano & M. Martínez-Ramón 10.1016/j.solener.2023.111968
- Integration Transformer for Ground-Based Cloud Image Segmentation S. Liu et al. 10.1109/TGRS.2023.3265384
- Ground-Based Cloud Image Segmentation Method Based on Improved U-Net D. Yin et al. 10.3390/app142311280
- Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning B. Kim et al. 10.5194/amt-14-6695-2021
- ACLNet: an attention and clustering-based cloud segmentation network D. Makwana et al. 10.1080/2150704X.2022.2097031
16 citations as recorded by crossref.
- Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset M. Krinitskiy et al. 10.3390/rs15071720
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- CloudRaednet: residual attention-based encoder–decoder network for ground-based cloud images segmentation in nychthemeron C. Shi et al. 10.1080/01431161.2022.2054298
- Explicit basis function kernel methods for cloud segmentation in infrared sky images G. Terrén-Serrano & M. Martínez-Ramón 10.1016/j.egyr.2021.08.020
- Improved RepVGG ground-based cloud image classification with attention convolution C. Shi et al. 10.5194/amt-17-979-2024
- A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism L. Zhang et al. 10.3390/rs14163970
- CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation C. Shi et al. 10.1016/j.energy.2024.133128
- Comparative analysis of methods for cloud segmentation in ground-based infrared images G. Terrén-Serrano & M. Martínez-Ramón 10.1016/j.renene.2021.04.141
- CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning C. Shi et al. 10.1007/s11063-021-10457-2
- Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems G. Terren-Serrano & M. Martinez-Ramon 10.1109/TGRS.2022.3154710
- CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation C. Shi et al. 10.1109/LGRS.2020.3009227
- Processing of global solar irradiance and ground-based infrared sky images for solar nowcasting and intra-hour forecasting applications G. Terrén-Serrano & M. Martínez-Ramón 10.1016/j.solener.2023.111968
- Integration Transformer for Ground-Based Cloud Image Segmentation S. Liu et al. 10.1109/TGRS.2023.3265384
- Ground-Based Cloud Image Segmentation Method Based on Improved U-Net D. Yin et al. 10.3390/app142311280
- Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning B. Kim et al. 10.5194/amt-14-6695-2021
- ACLNet: an attention and clustering-based cloud segmentation network D. Makwana et al. 10.1080/2150704X.2022.2097031
Latest update: 06 Jan 2025
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.
Cloud segmentation plays a very important role in astronomical observatory site selection. At...
