# Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers
## 4 Results and Discussion  
To verify the validity of the proposed method in this paper, we evaluated the performance of the model on the 10 cloud genera using various methods:  
1. [label=0., ref=0]
2. Whether the first three items of the classification result include the correct labels. 3. Classification by cloud texture (divided into Stratiformis, Undulatus, Cumulus). Refer to Table 1 for the specific method of classifying clouds based on their texture. In addition, we assessed the precision rate, recall rate, and F-measures for each evaluation method. Accuracy is concerned with the correctness of the objects classified in the model and is not dependent on whether the objects are fully labeled or have a high classification threshold. Recall is concerned with whether the model labels all the objects that belong to a certain class, regardless of whether other objects are misclassified. It is typically associated with a low classification threshold. The evaluation metrics are calculated based  
Figure 8: VGG16 structure. The network contains 16 convolutional layers, 5 pooling layers, and 1 fully connected layer. Figure 7: In the traditional neural network (NN) architecture, a node represents a neuron, \(\theta\) represents the weight parameter of the layer, and \(f(\theta)\) represents the activation function used by the neuron. NN is fully connected, where each neuron is connected to all neurons in the next layer. on True Positive (_tp_), True Negative (_tn_), False Positive (_fp_), and False Negative (_fn_) for the classification results generated by the model, as shown in Equations (8) and (9). \[Precision=\frac{tp}{tp+fp},Recall=\frac{tp}{tp+fn} \tag{8}\]  
\[ \begin{array}{ll}F-score(\beta)&=\frac{\left(1+\beta^{2}\right)Precision= Recall}{\beta^{2}*Precision+Recall}\\ &=\frac{\left(1+\beta^{2}\right)tp}{(1+\beta^{2})tp+\beta^{2}f^{2}p+f^{2}n} \end{array}  \tag{9}\]  
where \(\beta\) is the hyperparameter, usually set to 1. The result is considered optimal when the \(F-score(\beta)\) value is 1; 0 is considered the worst result. In this paper, we used the Sobel, Canny, Clahe, and Gamma methods, respectively, to process the SSC and generate four new datasets. These datasets were applied to the VGG16 model (Figure 8), and the classification results were evaluated using the precision rate, recall rate, F-measures, and accuracy mentioned above. The results indicated that the Gamma Correction dataset performed best, as shown in Table 2. The majority of cloud genera displayed high classification recall of 80% and precision. However, the classification results of Cirrus (Cs) were relatively poor. Upon examining the model's classification 20 times, the Cs labeled data sample model classification was shown in Figure 9, where the majority of misclassified results were identified as Altocumulus (As). It is because both cloud genera belong to Stratiformis and share high similarity in texture features, and the International Cloud Atlas distinguishes between them primarily based on the height of the cloud base and differences in composition. Based on the classification of cloud texture features, all three categories displayed over 70% correctness in both recall and precision. This indicates that the model has a strong discriminatory ability for cloud texture features. As shown in Table 3, the Clahe dataset has the second-best performance following the Gamma dataset. While the overall performance of Clahe in identifying cloud species is slightly better than Gamma, precision analysis shows that some cloud genera had high precision while others had low precision. There was a significant difference in the performance between the two parts of cloud genera. Our belief is that the Clahe dataset does not showcase cloud textures as effectively as Gamma, which is supported by its inferior performance in texture feature classification compared to Gamma. Upon comparing the recall of both, we observed that Stratiformis and Cumulus have significantly higher recall rates in Gamma compared to Clahe.