Articles | Volume 8, issue 3
Atmos. Meas. Tech., 8, 1173–1182, 2015
https://doi.org/10.5194/amt-8-1173-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 8, 1173–1182, 2015
https://doi.org/10.5194/amt-8-1173-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
10 Mar 2015
Research article
| 10 Mar 2015
Block-based cloud classification with statistical features and distribution of local texture features
H.-Y. Cheng and C.-C. Yu
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Cited
18 citations as recorded by crossref.
- Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition S. Liu et al. 10.3390/rs12030464
- 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
- Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks M. Li et al. 10.1109/ACCESS.2019.2916905
- Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning Q. Luo et al. 10.1155/2018/9684206
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. 10.1016/j.image.2020.115950
- Cloud classification of ground-based infrared images combining manifold and texture features Q. Luo et al. 10.5194/amt-11-5351-2018
- Deep tensor fusion network for multimodal ground-based cloud classification in weather station networks M. Li et al. 10.1016/j.adhoc.2019.101991
- Feature extraction techniques for ground-based cloud type classification T. Kliangsuwan & A. Heednacram 10.1016/j.eswa.2015.05.016
- Deep multimodal fusion for ground-based cloud classification in weather station networks S. Liu & M. Li 10.1186/s13638-018-1062-0
- Improving cloud type classification of ground-based images using region covariance descriptors Y. Tang et al. 10.5194/amt-14-737-2021
- ClouDet: A Dilated Separable CNN-Based Cloud Detection Framework for Remote Sensing Imagery H. Guo et al. 10.1109/JSTARS.2021.3114171
- Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques H. Cheng & C. Lin 10.5194/amt-10-199-2017
- Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. 10.1109/TGRS.2019.2917612
- From pixels to patches: a cloud classification method based on a bag of micro-structures Q. Li et al. 10.5194/amt-9-753-2016
- Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding J. Gan et al. 10.1109/JSTARS.2017.2669206
- FFT features and hierarchical classification algorithms for cloud images T. Kliangsuwan & A. Heednacram 10.1016/j.engappai.2018.08.008
- Automatic Cloud-Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas-Tato et al. 10.1002/2017JD027131
- Robust partitioning and indexing for iris biometric database based on local features E. Khalaf et al. 10.1049/iet-bmt.2017.0130
17 citations as recorded by crossref.
- Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition S. Liu et al. 10.3390/rs12030464
- 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
- Dual Guided Loss for Ground-Based Cloud Classification in Weather Station Networks M. Li et al. 10.1109/ACCESS.2019.2916905
- Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning Q. Luo et al. 10.1155/2018/9684206
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. 10.1016/j.image.2020.115950
- Cloud classification of ground-based infrared images combining manifold and texture features Q. Luo et al. 10.5194/amt-11-5351-2018
- Deep tensor fusion network for multimodal ground-based cloud classification in weather station networks M. Li et al. 10.1016/j.adhoc.2019.101991
- Feature extraction techniques for ground-based cloud type classification T. Kliangsuwan & A. Heednacram 10.1016/j.eswa.2015.05.016
- Deep multimodal fusion for ground-based cloud classification in weather station networks S. Liu & M. Li 10.1186/s13638-018-1062-0
- Improving cloud type classification of ground-based images using region covariance descriptors Y. Tang et al. 10.5194/amt-14-737-2021
- ClouDet: A Dilated Separable CNN-Based Cloud Detection Framework for Remote Sensing Imagery H. Guo et al. 10.1109/JSTARS.2021.3114171
- Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques H. Cheng & C. Lin 10.5194/amt-10-199-2017
- Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. 10.1109/TGRS.2019.2917612
- From pixels to patches: a cloud classification method based on a bag of micro-structures Q. Li et al. 10.5194/amt-9-753-2016
- Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding J. Gan et al. 10.1109/JSTARS.2017.2669206
- FFT features and hierarchical classification algorithms for cloud images T. Kliangsuwan & A. Heednacram 10.1016/j.engappai.2018.08.008
- Automatic Cloud-Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas-Tato et al. 10.1002/2017JD027131
1 citations as recorded by crossref.
Saved (final revised paper)
Latest update: 24 May 2022
Short summary
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we...
