# Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers
## 5 Conclusions  
In this paper, we propose a CNN-based method for identifying 10 cloud genera. Due to the reliance on the identification of the texture of the clouds, the images undergo pre-processing to enhance these features. The classical VGG16 architecture of the CNN model is then employed for training. We have also constructed a dataset of high-quality cloud images, known as the SSC. Moreover, the SSC is the first dataset to ensure an approximately equal number of data samples for all 10 cloud genera.  
Following multiple rounds of experimentation, the results demonstrate that the model presented in this paper can accurately identify cloud genera using ground-based cloud images and performs well on our newly established database. The model has some misclassification errors, mainly focused on As and Cs. This is due to the similarity in their texture features, as well as a transformation phenomenon, making it challenging to discriminate based on image texture alone. Additionally, cloud height is currently a critical indicator for distinguishing between these two cloud genera. We have also demonstrated that the model's performance can be optimized by adjusting the brightness or darkness of the images.  
At present, our research on cloud identification solely relies on cloud texture. However, there are numerous features that can be utilized to identify clouds, such as cloud height and composition. For the future, we are considering incorporating hardware equipment (e.g., the ceilometer) to enable us to collect cloud height data while capturing cloud images. We hope to address the difficulty in distinguishing between As and Cs by adding the feature of cloud height and improving the performance of the model. In addition, multiple cloud genera can often appear in the same sky simultaneously. In the next stage, we plan to implement distinguishing between different cloud genera in the same picture. Our research results have practical applications. For example, meteorological organizations can utilize our method to collect cloud data in unpopulated areas (e.g., plateaus, oceans, etc.). On the other hand, our method is highly real-time, allowing meteorological bureaus to monitor clouds in real-time using camera equipment to forecast future local weather changes. Furthermore, our method enables the public, without prior meteorological knowledge, to identify between different cloud genera.  
Conceptualization, Z.L. and H.K.; methodology, Z.L.; software, Z.L. and H.K.; investigation, H.K., Z.L. and C.-S.W.; resources, C.-S.W.; writing--original draft preparation, Z.L.; writing--review and editing, H.K. and Z.L.; visualization, Z.L.; supervision, H.K. and C.-S.W.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Macau Foundation under its Research Fund (Grant No. MF2102), Macau.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Recall** & **Precision** & **F1** & **Accuracy** \\ \hline AC & 0.9 & 0.9 & 0.9 & \\ AS & 0.95 & 0.34 & 0.5 & \\ CB & 0.8 & 1 & 0.89 & \\ CC & 0.6 & 1 & 0.75 & \\ CI & 0.85 & 1 & 0.92 & \\ CS & 0.7 & 1 & 0.82 & \\ CU & 0.65 & 0.76 & 0.7 & \\ NS & 0.55 & 0.67 & 0.6 & \\ SC & 1 & 0.29 & 0.45 & \\ ST & 0 & 0 & 0 & \\ \hline Stratiformis & 0.56 & 0.56 & 0.56 & \\ Undulatus & 0.66 & 0.54 & 0.59 & 0.54 \\ Cumulus & 0.25 & 0.5 & 0.33 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation of Canny datasets with multiple classification methods.