Articles | Volume 9, issue 2
https://doi.org/10.5194/amt-9-753-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-753-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Mar 2016
Research article |
| 01 Mar 2016
From pixels to patches: a cloud classification method based on a bag of micro-structures
Qingyong Li
CORRESPONDING AUTHOR
Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China
Zhen Zhang
Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China
Weitao Lu
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Jun Yang
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Ying Ma
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Wen Yao
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
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- Classification of Ground-Based Cloud Images by Contrastive Self-Supervised Learning Q. Lv et al. 10.3390/rs14225821
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition S. Liu et al. 10.3390/rs12030464
- FFT features and hierarchical classification algorithms for cloud images T. Kliangsuwan & A. Heednacram 10.1016/j.engappai.2018.08.008
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- Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. 10.1109/TGRS.2019.2917612
- Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks M. Li et al. 10.1109/ACCESS.2019.2916905
- Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging J. Luo et al. 10.5194/amt-17-3765-2024
- Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas‐Tato et al. 10.1002/2017JD027131
- DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features L. Ye et al. 10.1109/TGRS.2017.2712809
- A local binary pattern classification approach for cloud types derived from all-sky imagers S. Oikonomou et al. 10.1080/01431161.2018.1530807
- Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images J. Yang et al. 10.1175/JAMC-D-18-0095.1
- Cloud classification of ground-based infrared images combining manifold and texture features Q. Luo et al. 10.5194/amt-11-5351-2018
- Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding J. Gan et al. 10.1109/JSTARS.2017.2669206
- Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network S. Liu et al. 10.1029/2020GL087338
- Weather Forecast Based on Color Cloud Image Recognition under the Combination of Local Image Descriptor and Histogram Selection K. Tran-Trung et al. 10.3390/electronics11213460
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- Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning Q. Luo et al. 10.1155/2018/9684206
- Estimating Geo‐Referenced Cloud‐Base Height With Whole‐Sky Imagers B. Lyu et al. 10.1029/2019EA000944
- Intelligent classification of ground-based visible cloud images using a transfer convolutional neural network and fine-tuning M. Wang et al. 10.1364/OE.442455
- A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification Y. Wang et al. 10.1109/TGRS.2018.2866206
- CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network M. Wang et al. 10.1175/JTECH-D-19-0189.1
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. 10.1016/j.image.2020.115950
21 citations as recorded by crossref.
- Digital forensics of microscopic images for printed source identification M. Tsai & I. Yuadi 10.1007/s11042-017-4771-1
- Ground-Based Remote Sensing Cloud Classification via Context Graph Attention Network S. Liu et al. 10.1109/TGRS.2021.3063255
- Classification of Ground-Based Cloud Images by Contrastive Self-Supervised Learning Q. Lv et al. 10.3390/rs14225821
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition S. Liu et al. 10.3390/rs12030464
- FFT features and hierarchical classification algorithms for cloud images T. Kliangsuwan & A. Heednacram 10.1016/j.engappai.2018.08.008
- An all-sky camera image classification method using cloud cover features X. Li et al. 10.5194/amt-15-3629-2022
- Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. 10.1109/TGRS.2019.2917612
- Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks M. Li et al. 10.1109/ACCESS.2019.2916905
- Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging J. Luo et al. 10.5194/amt-17-3765-2024
- Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas‐Tato et al. 10.1002/2017JD027131
- DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features L. Ye et al. 10.1109/TGRS.2017.2712809
- A local binary pattern classification approach for cloud types derived from all-sky imagers S. Oikonomou et al. 10.1080/01431161.2018.1530807
- Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images J. Yang et al. 10.1175/JAMC-D-18-0095.1
- Cloud classification of ground-based infrared images combining manifold and texture features Q. Luo et al. 10.5194/amt-11-5351-2018
- Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding J. Gan et al. 10.1109/JSTARS.2017.2669206
- Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network S. Liu et al. 10.1029/2020GL087338
- Weather Forecast Based on Color Cloud Image Recognition under the Combination of Local Image Descriptor and Histogram Selection K. Tran-Trung et al. 10.3390/electronics11213460
- A Novel Method for Ground-Based Cloud Image Classification Using Transformer X. Li et al. 10.3390/rs14163978
- Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning Q. Luo et al. 10.1155/2018/9684206
- Estimating Geo‐Referenced Cloud‐Base Height With Whole‐Sky Imagers B. Lyu et al. 10.1029/2019EA000944
4 citations as recorded by crossref.
- Intelligent classification of ground-based visible cloud images using a transfer convolutional neural network and fine-tuning M. Wang et al. 10.1364/OE.442455
- A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification Y. Wang et al. 10.1109/TGRS.2018.2866206
- CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network M. Wang et al. 10.1175/JTECH-D-19-0189.1
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. 10.1016/j.image.2020.115950
Saved (final revised paper)
Latest update: 26 Jul 2024
Short summary
This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.
This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for...
