A Novel Computer-Aided Cloud Type Classification Method Based on Convolutional Neural Network with Squeeze-And- Excitation

Li, Guanshen

doi:10.1088/1742-6596/1802/3/032051

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies show that deep learning methods can adaptively learn the deep features of clouds and have higher detection accuracy than traditional machine learning methods [26][27][28][29][30][31]. Liu et al introduced a neural network for satellite cloud detection tasks, and conducted experiments on the FY-2C satellite cloud image dataset.…”

Section: Introductionmentioning

confidence: 99%

UATNet: U-Shape Attention-Based Transformer Net for Meteorological Satellite Cloud Recognition

et al. 2021

View full text Add to dashboard Cite

Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. However, different from ground-based cloud images with a small observation range and easy operation, satellite cloud images have a wider cloud coverage area and contain more surface features. Hence, it is difficult to effectively extract the structural shape, area size, contour shape, hue, shadow and texture of clouds through traditional deep learning methods. In order to analyze the regional cloud type characteristics effectively, we construct a China region meteorological satellite cloud image dataset named CRMSCD, which consists of nine cloud types and the clear sky (cloudless). In this paper, we propose a novel neural network model, UATNet, which can realize the pixel-level classification of meteorological satellite cloud images. Our model efficiently integrates the spatial and multi-channel information of clouds. Specifically, several transformer blocks with modified self-attention computation (swin transformer blocks) and patch merging operations are used to build a hierarchical transformer, and spatial displacement is introduced to construct long-distance cross-window connections. In addition, we introduce a Channel Cross fusion with Transformer (CCT) to guide the multi-scale channel fusion, and design an Attention-based Squeeze and Excitation (ASE) to effectively connect the fused multi-scale channel information to the decoder features. The experimental results demonstrate that the proposed model achieved 82.33% PA, 67.79% MPA, 54.51% MIoU and 70.96% FWIoU on CRMSCD. Compared with the existing models, our method produces more precise segmentation performance, which demonstrates its superiority on meteorological satellite cloud recognition tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

UATNet: U-Shape Attention-Based Transformer Net for Meteorological Satellite Cloud Recognition

et al. 2021

View full text Add to dashboard Cite

show abstract

Robust Information Center Implementation of College English Training under the Network Computer Aided System Environment Considering QoS

Wang

2022

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)

View full text Add to dashboard Cite

Research on ground-based cloud image classification combining local and global features

Zhang,

Zheng,

Zhang

et al. 2024

J. Electron. Imag.

View full text Add to dashboard Cite

Clouds are an important factor in predicting future weather changes. Cloud image classification is one of the basic issues in the field of ground-based cloud meteorological observation. Deep CNN mainly focuses on the local receptive field, and the processing of global information may be relatively weak. In ground-based cloud image classification, if there is a complex background, it will help to better model the long-range dependence of the image if the relationship between different locations in the image can be globally captured. A ground-based cloud image classification method is proposed based on the fusion of local features and global features (LG_CloudNet). The ground-based cloud image classification method integrates the global feature extraction module (GF_M) and the local feature extraction module (LF_M), using the attention mechanism to weight and merge features, respectively. The LG_CloudNet model enables richer and comprehensive feature representation at lower computational complexity. In order to ensure the learning and generalization capabilities of the model during training, AdamW (Adam weight decay) is combined with learning rate warm-up and stochastic gradient descent with warm restarts methods to adjust the learning rate. The experimental results demonstrate that the proposed method achieves favorable ground-based cloud image classification outcomes and exhibits robust performance in classifying cloud images. In the datasets of GCD, CCSN, and ZNCL, the classification accuracy is 94.94%, 95.77%, and 98.87%, respectively.

show abstract

A Novel Computer-Aided Cloud Type Classification Method Based on Convolutional Neural Network with Squeeze-And- Excitation

Cited by 3 publications

References 10 publications

UATNet: U-Shape Attention-Based Transformer Net for Meteorological Satellite Cloud Recognition

UATNet: U-Shape Attention-Based Transformer Net for Meteorological Satellite Cloud Recognition

Robust Information Center Implementation of College English Training under the Network Computer Aided System Environment Considering QoS

Research on ground-based cloud image classification combining local and global features

Contact Info

Product

Resources

About