## 4 Experiments and Discussion
### Ablation Experiments
#### 4.5.1 Performance Analysis from Different Modules
After integrating the PSFM and GFM, the model's accuracy was balanced in both small object and large area segmentation, demonstrating the model's ability to optimize both global and local features in a collaborative manner. The Potsdam dataset, known for its high resolution and diverse categories, is ideal for model performance evaluation. The experimental results in Table 4 indicate that removing the PSFM reduces mIoU from 70.16% to 63.65%, significantly impairing small object segmentation, notably for \"car\" categories, where IoU drops by 13.89%. This again demonstrates that the PSFM is particularly beneficial for enhancing fine-scale structure and small object boundaries in RGB-based segmentation. Removing the GFM further decreases mIoU to 60.63%, highlighting its importance for large-scale semantic classification and maintaining global structural consistency. The \"tree\" and \"building\" categories, for instance, exhibit more fragmented and spatially incoherent predictions when the GFM is removed. The combined removal of both modules results in a 9.53% IoU decrease and an 11.35% F1 drop. Using both PSFM and GFM together markedly improves the handling of complex structures, enhancing feature fusion and spatial consistency in challenging RGB scenes. The visual results presented in Figure 10 further validate the quantitative findings discussed above. PSFM exhibits outstanding performance in small object detection and multi-scale scenarios, effectively preserving object shapes and contours even in cases with blurred boundaries. GFM plays a critical role in global semantic modeling, enhancing semantic consistency and capturing long-range dependencies, although its impact is slightly less pronounced compared to PSFM. The complete PLGTransformer model integrates both local and global information streams, resulting in improved segmentation accuracy and better generalization across datasets. Together, the quantitative and visual results \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Imp. Surf. (\%)**} & \multicolumn{2}{c}{**Building (\%)**} & \multicolumn{2}{c}{**Car (\%)**} & \multicolumn{2}{c}{**Low Vog. (\%)**} & \multicolumn{2}{c}{**Tree (\%)**} & \multicolumn{2}{c}{**mIoU (\%)**} & \multicolumn{2}{c}{**mIoU (\%)**} & \multicolumn{2}{c}{**mIoU (\%)**} \\ \cline{2-11}  & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** \\ \hline Base & 59.52 & 69.56 & 79.72 & 64.58 & 40.61 & 45.91 & 52.27 & 63.12 & 41.01 & 51.08 & 50.63 & 58.85 \\ Base+PSFM & 61.18 & 71.20 & 78.76 & 82.10 & 42.35 & 50.52 & 61.87 & 73.26 & 61.00 & 79.07 & 60.63 & 69.65 \\ Base+GFM & 62.26 & 72.06 & 75.00 & 80.57 & 56.55 & 62.58 & 58.73 & 70.19 & 65.70 & 76.22 & 63.65 & 72.32 \\ Base+PSFM+CGM & 58.90 & 74.14 & 85.21 & 92.01 & 70.44 & 82.66 & 61.11 & 70.40 & 75.14 & 85.81 & 70.16 & 81.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation results from different modules on the Potsdam dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Imp. Surf. (\%)**} & \multicolumn{2}{c}{**Building (\%)**} & \multicolumn{2}{c}{**Car (\%)**} & \multicolumn{2}{c}{**Low Vog. (\%)**} & \multicolumn{2}{c}{**Tree (\%)**} & \multicolumn{2}{c}{**mIoU (\%)**} & \multicolumn{2}{c}{**mIoU (\%)**} \\ \cline{2-11}  & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** \\ \hline Base & 65.75 & 77.08 & 77.51 & 81.24 & 18.05 & 26.36 & 84.10 & 59.61 & 61.01 & 73.89 & 82.29 & 63.64 \\ Base+PSFM & 67.28 & 78.41 & 78.20 & 86.81 & 6.90 & 11.24 & 50.08 & 64.20 & 61.90 & 74.27 & 53.09 & 63.09 \\ Base+GFM & 70.57 & 80.66 & 79.66 & 87.19 & 29.19 & 39.27 & 53.71 & 67.62 & 67.07 & 78.35 & 60.04 & 70.62 \\ Base+PSFM+CGM & 81.46 & 88.45 & 77.64 & 87.41 & 59.05 & 74.26 & 73.38 & 84.65 & 72.74 & 84.22 & 72.85 & 83.80 \\ \hline \hline \end{tabular}
\end{table}