## 4 Experiments and Discussion
### Ablation Experiments
#### 4.5.4 Visualization Analysis  
To further validate the feature information that the PLGTransformer model focuses on in a specific convolutional layer, the Grad-CAM [53] method can be applied. Grad-CAM utilizes the gradients with respect to the target class to generate a heatmap by computing the weighted sum of the feature maps from the last convolutional layer. This method creates a weighted combination of the feature maps, where the weights correspond to the importance of each feature map for the classification decision. This heatmap highlights the regions of the image that contribute the most to the predicted class.  
As shown in Figure 11, in the building class, the model mainly focuses on the locations of buildings in the image. In the low vegetation class, the model focuses on sparse vegetation areas. In the impervious surface class, the model focuses on the hard-covered  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Varied** & **Value** & **Fixed Parameters** & **mIoU (\%)** & **mF1 (\%)** \\ \hline \multirow{8}{*}{\(\alpha_{\text{region}}\)} & 0.25 & \(\beta=0.3\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 71.63 & 82.57 \\  & 0.5 & \(\beta=0.3\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 72.80 & 84.02 \\  & 1.0 & \(\beta=0.3\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 72.85 & 83.80 \\  & 2.0 & \(\beta=0.3\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 72.62 & 83.66 \\  & 5.0 & \(\beta=0.3\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 71.54 & 81.44 \\ \hline \multirow{8}{*}{\(\beta\)} & 0.1 & \(\alpha_{\text{region}}=1.0\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 67.25 & 77.05 \\  & 0.3 & \(\alpha_{\text{region}}=1.0\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 72.85 & 83.80 \\  & 0.5 & \(\alpha_{\text{region}}=1.0\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 71.98 & 82.79 \\  & 0.7 & \(\alpha_{\text{region}}=1.0\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 68.64 & 76.55 \\  & 0.9 & \(\alpha_{\text{region}}=1.0\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\) & 64.26 & 75.96 \\ \hline \multirow{8}{*}{\(\gamma\)} & 0.0 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\alpha_{\text{boundary}}=0.3\) & 71.64 & 82.57 \\  & 1.0 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\alpha_{\text{boundary}}=0.3\) & 72.45 & 83.68 \\ \cline{1-1}  & 2.0 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\alpha_{\text{boundary}}=0.3\) & 72.85 & 83.80 \\ \cline{1-1}  & 3.0 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\alpha_{\text{boundary}}=0.3\) & 72.51 & 83.69 \\ \cline{1-1}  & 5.0 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\alpha_{\text{boundary}}=0.3\) & 71.76 & 82.61 \\ \hline \multirow{8}{*}{\(\alpha_{\text{boundary}}\)} & 0.1 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\gamma=2.0\) & 71.12 & 81.92 \\  & 0.2 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\gamma=2.0\) & 72.20 & 82.88 \\ \cline{1-1}  & 0.3 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\gamma=2.0\) & 72.85 & 83.80 \\ \cline{1-1}  & 0.5 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\gamma=2.0\) & 71.78 & 82.41 \\ \cline{1-1}  & 1.0 & \(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\gamma=2.0\) & 70.50 & 80.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter sensitivity analysis of the compound loss on the Vaihingen dataset.  
ground areas. This demonstrates that Grad-CAM can effectively visualize the key regions of interest, helping to explain the model's classification decisions.