Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion

Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

IEEE Access · 01 January 2020 · China Meteorological Administration (CMA)

In one sentence. Retraction notice for a CNN-based ground-based cloud image recognition system that duplicated prior work; the underlying topic is cloud classification.

Abstract

Notice of Violation of IEEE Publication Principles“Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion”by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, and Yang Binin IEEE Access, September 2020After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.This paper duplicates content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.“DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features”by Liang Ye, Zhiguo Cao, and Yang Xiaoin the IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 10, October 2017The 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.

Labels

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.