## 4 Experiments and Results
### Comparison of Classification Results of the WaveNet Network Using Different Training Sets  
This article evaluates the WaveNet model using the overall accuracy (OA) and average accuracy (AA) metrics for classification. The confusion matrix \(S\) is a \(L\times L\) matrix, \(L\) that represents the number of classes. \(S_{ij}\) represents the number of test samples that belong to the class i and were classified as a class \(j\). The total number of test samples is \(M=\sum_{i}^{L}\sum_{j}^{L}S_{ij}\). The overall accuracy (OA) metric provides a good description of the overall classification accuracy, where OA is calculated by dividing the number of correctly classified samples by the total number of test samples, which can be represented as follows:  
\[\text{OA}=\frac{\sum_{i}^{L}S_{i,j}}{M}\times 100\% \tag{10}\]  
The average accuracy (AA) metric provides a good description of the classification performance differences among each class, representing the average classification accuracy for each class, which can be represented as follows:  
\[\text{AA}=\frac{\sum_{i}^{L}(\frac{S_{ij}}{\sum_{i}^{L}S_{ij}})}{L}\times 100\% \tag{11}\]  
Figure 8: T-SNE dimensionality reduction is applied to different data augmentation datasets, where red represents the data generated by the modified DCGAN and blue represents the original data. Precision is a metric widely used to evaluate the performance of classification models. It measures the accuracy of the model in predicting positive examples. The calculation formula of accuracy is as follows:  
\[Precision_{i}=\frac{S_{i,i}}{\sum_{j}^{L}S_{j,i}}\times 100\% \tag{12}\]  
In this formula, \(Precision_{i}\) represents the precision for class \(i\). \(S_{i,i}\) denotes the number of samples correctly classified as class i, and \(\sum_{j}^{L}S_{j,i}\) represents the total number of samples predicted as class \(i\). Recall is also one of the indicators widely used to evaluate the performance of classification models. It measures the proportion of actual positive examples identified by the model, that is, how many of all actual positive examples were correctly predicted as positive by the model. The calculation formula of recall rate is as follows:  
\[Recall_{i}=\frac{S_{i,i}}{\sum_{j}^{L}S_{i,j}}\times 100\% \tag{13}\]  
In this formula, \(Recall_{i}\) represents the recall for class \(i\). \(S_{i,i}\) denotes the number of samples correctly classified as class \(i\), and \(\sum_{j}^{L}S_{i,j}\) represents the total number of samples actually belonging to class \(i\). To investigate whether the images generated by the modified DCGAN network possess features similar to those of real images and whether they are more effective as a data augmentation technique than traditional methods, enhancing the network's generalization ability, we conducted a series of comparative experiments. The overall accuracy (OA) and average accuracy (AA) experimental results are shown in Table 6. The precision of each category is shown in Table 7. The recall rates of each category are shown in Table 8.  
By using the modified DCGAN data augmentation method proposed in this paper, we observed an overall improvement of 5.437% in the classification accuracy of the test set, with all four types of remote sensing images showing increased recognition rates. Specifically, the recognition accuracy of clouds, land, oceans and internal waves improved by 2.75%, 6.5%, 8.25%, and 4.25%, respectively. Thanks to the modified DCGAN data \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Traditional Data** & **Data Augmentation Based** \\  & **Augmentation/\%** & **on the Modified DCGAN/\%** \\ \hline Overall accuracy & 93.188 & 98.625 \\ Accuracy of cloud recognition & 97.000 & 99.750 \\ Accuracy of land recognition & 90.750 & 97.250 \\ Accuracy of ocean recognition & 90.250 & 98.500 \\ Accuracy of oceanic internal waves recognition & 94.750 & 99.000 \\ \hline \hline \end{tabular}
\end{table}