Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion
The recognition of ground-based cloud images has rich application prospects in many aspects such as weather prediction, astronomical site selection, and meteorological observation. Affected by factors such as rotation and illumination, the traditional feature extraction method is difficult to accura...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9205205/ |
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| _version_ | 1850174579887046656 |
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| author | Ma Jingyi Tiejun Zhang Jing Guodong Yan Wenjun Yang Bin |
| author_facet | Ma Jingyi Tiejun Zhang Jing Guodong Yan Wenjun Yang Bin |
| author_sort | Ma Jingyi |
| collection | DOAJ |
| description | The recognition of ground-based cloud images has rich application prospects in many aspects such as weather prediction, astronomical site selection, and meteorological observation. Affected by factors such as rotation and illumination, the traditional feature extraction method is difficult to accurately describe the features of cloud images, resulting in low accuracy of ground-based cloud image recognition, and cannot meet the requirements of practical applications. With the popularity of convolutional neural networks in image processing, ground-based cloud image recognition algorithms based on convolutional neural network have become a research focus. However, the features of the ground-based cloud image are relatively shallow, and the cloud texture and other features are seriously lost in the convolution process, and it is difficult to achieve a good recognition effect. This paper proposes a ground-based cloud image recognition system based on multi-scale convolutional neural network (Multi-CNN) and multilayer perceptron neural networks (MLP). The multi-level and multi-scale convolution feature extraction is performed through convolution layers of Multi-CNN, and the local features with strong resolving power are selected through the feature screening algorithm based on DP clustering. Finally, the local features are encoded and fused for cloud image classification based on MLP. Filed test results show that our method was superior to other tested network models in terms of the recognition accuracy of 94.8% under 9 classification. In addition, ablation experiments show that the multi-scale feature extraction, screening and local feature coding in this paper have a significant effect on improving the algorithm’s ability to distinguish different cloud images. |
| format | Article |
| id | doaj-art-8f42e0075d4047c181ae128356c33ba4 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8f42e0075d4047c181ae128356c33ba42025-08-20T02:19:38ZengIEEEIEEE Access2169-35362020-01-01817394917396010.1109/ACCESS.2020.30263649205205Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and FusionMa Jingyi0https://orcid.org/0000-0002-7420-2719Tiejun Zhang1https://orcid.org/0000-0002-1660-6571Jing Guodong2Yan Wenjun3https://orcid.org/0000-0002-7263-2475Yang Bin4https://orcid.org/0000-0002-3538-4513Gansu Branch, China Meteorological Administration Training Centre, Beijing, ChinaInstitute of Arid Meteorology, China Meteorological Administration, Beijing, ChinaChina Meteorological Administration Training Center, Beijing, ChinaGansu Branch, China Meteorological Administration Training Centre, Beijing, ChinaGansu Branch, China Meteorological Administration Training Centre, Beijing, ChinaThe recognition of ground-based cloud images has rich application prospects in many aspects such as weather prediction, astronomical site selection, and meteorological observation. Affected by factors such as rotation and illumination, the traditional feature extraction method is difficult to accurately describe the features of cloud images, resulting in low accuracy of ground-based cloud image recognition, and cannot meet the requirements of practical applications. With the popularity of convolutional neural networks in image processing, ground-based cloud image recognition algorithms based on convolutional neural network have become a research focus. However, the features of the ground-based cloud image are relatively shallow, and the cloud texture and other features are seriously lost in the convolution process, and it is difficult to achieve a good recognition effect. This paper proposes a ground-based cloud image recognition system based on multi-scale convolutional neural network (Multi-CNN) and multilayer perceptron neural networks (MLP). The multi-level and multi-scale convolution feature extraction is performed through convolution layers of Multi-CNN, and the local features with strong resolving power are selected through the feature screening algorithm based on DP clustering. Finally, the local features are encoded and fused for cloud image classification based on MLP. Filed test results show that our method was superior to other tested network models in terms of the recognition accuracy of 94.8% under 9 classification. In addition, ablation experiments show that the multi-scale feature extraction, screening and local feature coding in this paper have a significant effect on improving the algorithm’s ability to distinguish different cloud images.https://ieeexplore.ieee.org/document/9205205/ |
| spellingShingle | Ma Jingyi Tiejun Zhang Jing Guodong Yan Wenjun Yang Bin Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion IEEE Access |
| title | Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion |
| title_full | Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion |
| title_fullStr | Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion |
| title_full_unstemmed | Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion |
| title_short | Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion |
| title_sort | notice of violation of ieee publication principles ground based cloud image recognition system based on multi cnn and feature screening and fusion |
| url | https://ieeexplore.ieee.org/document/9205205/ |
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