Articles | Volume 10, issue 1
https://doi.org/10.5194/amt-10-199-2017
© Author(s) 2017. 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-10-199-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Jan 2017
Research article |
| 17 Jan 2017
Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques
Hsu-Yung Cheng
CORRESPONDING AUTHOR
Department of Computer Science and Information Engineering, National Central University, No. 300 Jhongda Rd., Jhongli City, Taoyuan 32001, Taiwan
Chih-Lung Lin
Department of Electronic Engineering, Hwa Hsia University of Technology, New Taipei City, Taiwan
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Cited
24 citations as recorded by crossref.
- Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model H. Zuo et al. 10.1016/j.egyr.2022.03.182
- A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images X. Li et al. 10.3390/atmos10110640
- Pixel‐Based Image Processing for CIE Standard Sky Classification through ANN D. Granados-López et al. 10.1155/2021/2636157
- 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
- Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images L. Krauz et al. 10.3390/rs12111902
- Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems G. Terren-Serrano & M. Martinez-Ramon 10.1109/TGRS.2022.3154710
- Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation S. Hensel et al. 10.3390/en14196156
- Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery M. Xia et al. 10.1109/ACCESS.2021.3114185
- Cloud Segmentation, Validation of Weather Data, and Precipitation Prediction Using Machine Learning Algorithms N. Rajendiran et al. 10.1007/s13369-023-08611-0
- A Self Training Mechanism With Scanty and Incompletely Annotated Samples for Learning‐Based Cloud Detection in Whole Sky Images L. Ye et al. 10.1029/2022EA002220
- Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods F. Lin et al. 10.1016/j.ijforecast.2021.11.002
- 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
- Applying self-supervised learning for semantic cloud segmentation of all-sky images Y. Fabel et al. 10.5194/amt-15-797-2022
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al. 10.5194/amt-12-4713-2019
- 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
- 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
- Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. 10.1109/TGRS.2019.2917612
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. 10.1016/j.image.2020.115950
- Cloud detection in satellite images with classical and deep neural network approach: A review R. Gupta & S. Nanda 10.1007/s11042-022-12078-w
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. 10.1175/JTECH-D-21-0159.1
- Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas‐Tato et al. 10.1002/2017JD027131
- Introducing two Random Forest based methods for cloud detection in remote sensing images N. Ghasemian & M. Akhoondzadeh 10.1016/j.asr.2018.04.030
- Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network H. Cheng & C. Yu 10.3390/en17133073
24 citations as recorded by crossref.
- Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model H. Zuo et al. 10.1016/j.egyr.2022.03.182
- A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images X. Li et al. 10.3390/atmos10110640
- Pixel‐Based Image Processing for CIE Standard Sky Classification through ANN D. Granados-López et al. 10.1155/2021/2636157
- 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
- Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images L. Krauz et al. 10.3390/rs12111902
- Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems G. Terren-Serrano & M. Martinez-Ramon 10.1109/TGRS.2022.3154710
- Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation S. Hensel et al. 10.3390/en14196156
- Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery M. Xia et al. 10.1109/ACCESS.2021.3114185
- Cloud Segmentation, Validation of Weather Data, and Precipitation Prediction Using Machine Learning Algorithms N. Rajendiran et al. 10.1007/s13369-023-08611-0
- A Self Training Mechanism With Scanty and Incompletely Annotated Samples for Learning‐Based Cloud Detection in Whole Sky Images L. Ye et al. 10.1029/2022EA002220
- Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods F. Lin et al. 10.1016/j.ijforecast.2021.11.002
- 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
- Applying self-supervised learning for semantic cloud segmentation of all-sky images Y. Fabel et al. 10.5194/amt-15-797-2022
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al. 10.5194/amt-12-4713-2019
- 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
- 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
- Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. 10.1109/TGRS.2019.2917612
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. 10.1016/j.image.2020.115950
- Cloud detection in satellite images with classical and deep neural network approach: A review R. Gupta & S. Nanda 10.1007/s11042-022-12078-w
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. 10.1175/JTECH-D-21-0159.1
- Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas‐Tato et al. 10.1002/2017JD027131
- Introducing two Random Forest based methods for cloud detection in remote sensing images N. Ghasemian & M. Akhoondzadeh 10.1016/j.asr.2018.04.030
- Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network H. Cheng & C. Yu 10.3390/en17133073
Latest update: 23 Nov 2024
Short summary
A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.
A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection...
