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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Autores principales:	Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8f42e0075d4047c181ae128356c33ba4
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spelling	oai:doaj.org-article:8f42e0075d4047c181ae128356c33ba42021-11-19T00:06:38ZNotice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion2169-353610.1109/ACCESS.2020.3026364https://doaj.org/article/8f42e0075d4047c181ae128356c33ba42020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9205205/https://doaj.org/toc/2169-3536The 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.Ma JingyiTiejun ZhangJing GuodongYan WenjunYang BinIEEEarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 173949-173960 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 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
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
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
title	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_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_sort	notice of violation of ieee publication principles: ground-based cloud image recognition system based on multi-cnn and feature screening and fusion
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/8f42e0075d4047c181ae128356c33ba4
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Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion

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