## 4 Experiments and Discussion
### Comparative Experiments  
We compare the PLGTransformer with several state-of-the-art remote sensing image segmentation methods on two different datasets. The selected models for comparison include SwinT [21], Swin UNet [49], BANet [50], FTUNetFormer [51], and CTFuse [52]. As shown in Table 1, on the Vaihingen dataset, the PLGTransformer achieves performance scores of 72.85% for mean intersection over union (mIoU) and 83.80% for mean F1 score (mF1). Compared to the baseline model CTFuse, PLGTransformer improves by 1.45% in mIoU and 1.11% in mF1. The table highlights that our model excels in the categories of impermeable surfaces, buildings, low vegetation, and trees. Figure 6 presents the visual results obtained by all six methods on the Vaihingen dataset. \begin{table}
\begin{tabular}{l 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 Veg. (\%)**} & \multicolumn{2}{c}{**Tree (\%)**} & \multicolumn{2}{c}{**mIoU (\%)**} & \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 SwinT & 72.74 & 82.27 & 81.90 &  
On the Potsdam dataset, as shown in Table 2, the PLGTransformer achieves performance scores of 70.16% for mean intersection over union (mIoU) and 81.00% for mean F1 score (mF1). Compared to the baseline model CTFuse, PLGTransformer improves by 3.12% in mIoU and 1.78% in mF1. Figure 7 presents the visual results obtained by all six methods on the Potsdam dataset. Similar to the results on the Vaihingen dataset, our PLGTransformer performs better than other state-of-the-art models in handling fine details. It is evident that, compared to other state-of-the-art models, our PLGTransformer performs better in recognizing fine details and handling edges. Specifically, the CNN + Swin transformer parallel encoding structure effectively combines local texture information with global semantic context, providing a stronger receptive field and region  
Figure 6: Image segmentation comparisons on the Vaihingen dataset. In the figure, the following are shown: (**1**) original image; (**2**) labels; (**3**) SwinT; (**4**) Swin UNet; (**5**) BANet; (**6**) FTUNetFormer; (**7**) CTFuse; (**8**) PLGTransformer. Figure 7: Image segmentation comparisons on the Potsdam dataset. In the figure, the following are shown: (**1**) original image; (**2**) labels; (**3**) SwinT; (**4**) Swin UNet; (**5**) BANet; (**6**) FTUNetFormer; (**7**) CTFuse; (**8**) PLGTransformer. discrimination ability. As observed in Figure 6, the dual-branch encoding structure allows the model to maintain clear boundary expressions when processing complex scenes, such as \"building-road\" and \"building-low vegetation\" adjacent areas. Moreover, the pyramid self-learning fusion module (PSFM) in the decoding stage adapts and guides the fusion of multi-scale features, allowing the model to flexibly focus on important regions at different scales. The model effectively captures the subtle semantic transitions between buildings and surrounding low vegetation, achieving continuous edges and complete form segmentation of building objects, significantly outperforming other comparison methods. The integrated GFM models structural relationships between objects in the high-level semantic space, enhancing semantic consistency and reducing errors like object fragmentation. In conclusion, the proposed model excels in segmentation accuracy and shows superior generalization in structural continuity, boundary completeness, and small object recognition. To further validate the training process and convergence behavior of the proposed model, we continuously monitored the loss values on both the training and validation sets throughout the optimization process.