# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 5 Conclusions  
In this paper, a combined network-based ground-based cloud images classification method is proposed. Specifically, the ResNet50 and VGG16 networks are combined using decision fusion algorithm, which uses dual weights to weight the output of the sub-network. In addition, to optimize the parameters of the two sub-networks to approach synchronization, overlap pooling is used to replace the original VGG16 pooling layer. At the same time, the ECA-WS module is embedded after the pooling layer to improve the cross-channel interaction capability of the network. We constructed the NRELCD dataset that meets the actual application scenarios and used the MGCD dataset to verify the advanced nature of the network model.  
At present, our classification of clouds is only based on image features. In reality, there are many physical characteristics that can provide a basis for cloud classification, such as height, thickness, and speed. In the future, we will consider using these parameters in classification research to improve the performance of the model.  
All authors made significant contributions to the manuscript. [PERSON] and [PERSON] conceived, designed, and performed the experiments, and wrote the paper; [PERSON] and [PERSON] collected data and performed the experiments; [PERSON] analyzed the data and performed the experiments; [PERSON], [PERSON] and [PERSON] revised the paper and provided the background knowledge of cloud classification. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **MGCD** & **NRELCD** \\ \hline Method [4] & 68.90\% & 75.61\% \\ Method [9] & 81.14\% & 92.17\% \\ Method [7] & 87.90\% & - \\ Method [11] & 88.63\% & - \\ ResNet50 & 88.05\% & 94.04\% \\ VGG16 & 87.20\% & 94.15\% \\ GoogleNet & 87.53\% & 93.54\% \\ Inception\_v3 & 88.32\% & 94.20\% \\ MobileNet\_v2 & 86.92\% & 93.73\% \\ Ours & 90.08\% & 95.60\% \\ \hline \hline \end{tabular} Note. References [7; 11] use the meteorological data in MGCD, and NRELCD does not contain meteorological data.  
\end{table}
Table 4: Comparative experiment of classification methods.  
**Funding:** This research was funded by the Key Research and Development Project of Zhejiang Province, Grant Number 2021C04030, and the Public Project of Zhejiang Province, Grant Number LGG21F030004.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data collected in this study came from the National Renewable Energy Laboratory, available at [[https://midcdmz.nrel.gov/apps/sitehome.pl?site=BMS](https://midcdmz.nrel.gov/apps/sitehome.pl?site=BMS)]([https://midcdmz.nrel.gov/apps/sitehome.pl?site=BMS](https://midcdmz.nrel.gov/apps/sitehome.pl?site=BMS)) (accessed on 1 May 2021). The MGCD dataset can be obtained from ([EMAIL_ADDRESS]).  
**Acknowledgments:** The all-sky cloud images in the NRELCD dataset were obtained by the National Renewable Energy Laboratory in the United States. The MGCD dataset used in the experiment was also allowed by [PERSON]'s team. We would like to express our sincere thanks.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.