CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning-Reference-Cited by-同舟云学术

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CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning

Published:2021-04-26 Issue:4 Volume:53 Page:2715-2728
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Shi Chaojun,Zhou Yatong^ORCID,Qiu Bo

Funder

National Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS) under Grant

Hebei Province Foundation of Returned oversea scholars

Hebei Province Natural Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-021-10457-2.pdf

Reference38 articles.

1. Stephens GL (2005) Cloud feedbacks in the climate system: a critical review. J Clim 18(2):237–273

2. Klebe DI, Blatherwick RD, Morris VR (2014) Ground-based all-sky mid-infrared and visible imagery for purposes of characterizing cloud properties. Atmos Meas Tech 7(2):637–645

3. Yang J, Lu WT, Ma Y, Yao W (2012) An automated cirrus cloud detection method for a ground-based cloud image. J Atmos Ocean Technol 29(4):527–537

4. Cazorla A, Olmo FJ, Alados-Arboledasl L (2008) Development of a sky imager for cloud cover assessment. J Opt Soc Am A Opt Image Sci Vis 25(1):29–39

5. Kurtz B, Mejia F, Kleissl J (2017) A virtual sky imager testbed for solar energy forecasting. Sol Energy 158:753–759

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1. Research on ground-based cloud image classification combining local and global features;Journal of Electronic Imaging;2024-07-27

2. CloudFU-Net: A Fine-Grained Segmentation Method for Ground-Based Cloud Images Based on an Improved Encoder–Decoder Structure;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey;Renewable and Sustainable Energy Reviews;2024-01

4. Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network;Atmospheric Measurement Techniques;2023-11-13

5. CloudDeepLabV3+: a lightweight ground-based cloud segmentation method based on multi-scale feature aggregation and multi-level attention feature enhancement;International Journal of Remote Sensing;2023-08-03

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