# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 4 Experimental Results and Discussion  
### Results of GCI Classification  
The classification results for each class in different datasets and the overall classification accuracy are provided in Tables 5-7. It can be seen that the proposed method achieves accuracies of 94.24%, 92.73%, and 93.57% in the ASGC, CCSN, and GCD datasets, respectively. The precision and recall are all greater than 89% for all classes except Sc in the ASGC dataset, where the highest accuracy is 99.66% for Cu, and the highest recall is 100% for Cl, but the accuracy and recall are both less than 90% for Sc. The seven types of images are sorted according to the F1_score value from largest to smallest, and the order is Cl, Cu, Mi, Ac, Ci, Cb, and Sc. In the CCSN dataset, the precision and recall are greater than 87% for all classes. The highest precision is 97.18% for Cb, and the highest recall is 98.67% for Ct, but the lowest precision and recall are 87.21% and 88.67%, respectively, for St. The various types of images are sorted according to the F1_score value from largest to smallest, and the order is Ct, Ci, Cb, Ac, Cu, As, Cc, Ns, Sc, Cs, and St. Both the precision and recall of all classes are greater than 85% in the GCD dataset. Among them, the highest precision is 98.62% for Cu, and the highest recall is 99.33% for Cl, but the lowest precision is 89.49% for Cb, and the lowest recall is 85.33% for Sc. All types of images are sorted according to the F1_score value from largest to smallest, and the order is Cl, Cu, Ac, Ci, Mi, Cb, and Sc. In conclusion, the method can better identify various types of cloud images and has the ability to automatically classify cloud images. To analyze misclassification, the classification confusion matrix of different datasets is obtained as shown in Figure 11. The horizontal axis in each figure indicates the true image class, the vertical axis indicates the predicted image class, and the values present in the nondiagonal elements represent the number of misclassifications between classes. From the figure, it can be seen that, in the ASGC and GCD dataset, images of the Cl class are correctly classified the most, and the misclassified images are mainly from Cb and Sc, which is due to the fact that some images of the Sc class are affected by illumination, causing the bottom of the clouds to become dark black and thus easily confused with Cb. In addition, the movement of clouds can lead to changes in the shooting viewpoint, which increases the difficulty of identification. In the CCSN dataset, images of the Ct class are correctly classified in the largest number. The misclassified images are mainly from St, Ns, and Sc, which is predictable because they all belong to low-level clouds with a relatively similar structure and transparency. \begin{table}
\begin{tabular}{c c c c c c} \hline
**No** & **Class** & **Pr (\%)** & **Re (\%)** & **F1\_score (\%)** & **Acc (\%)** \\ \hline
1 & Ac & 96.17 & 92.00 & 94.04 & \\
2 & As & 93.27 & 92.33 & 92.80 & \\
3 & Cb & 97.18 & 92.00 & 94.52 & \\
4 & Cc & 93.24 & 92.00 & 92.62 & \\
5 & Ci & 92.65 & 96.67 & 94.62 & \\
6 & Cs & 89.14 & 90.33 & 89.73 & 92.73 \\
7 & Ct & 96.10 & 98.67 & 97.37 & \\
8 & Cu & 92.79 & 94.33 & 93.55 & \\
9 & Ns & 92.26 & 91.33 & 91.79 & \\
10 & Sc & 90.46 & 91.67 & 91.06 & \\
11 & St & 87.21 & 88.67 & 87.93 & \\ \hline \end{tabular}
\end{table} 
Table 6: The classification results for the CCSN dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Class** & **Pr (\%)** & **Re (\%)** & **F1\_score (\%)** & **Acc (\%)** \\ \hline
1 & Ac & 96.32 & 96.00 & 96.16 & \\
2 & Cb & 89.49 & 88.00 & 88.74 & \\
3 & Ci & 91.9 & 98.33 & 95.01 & \\
4 & Cl & 97.07 & 99.33 & 98.19 & 93.57 \\
5 & Cu & 98.62 & 95.00 & 96.78 & \\
6 & Mi & 90.88 & 93.00 & 91.93 & \\
7 & Sc & 90.78 & 85.33 & 87.97 & \\ \hline \end{tabular}
\end{table} 
Table 7: The classification results for the GCD dataset.