## 4 Experiments and Discussion
### Ablation Experiments  
#### 4.5.1 Performance Analysis from Different Modules  
To validate the effectiveness of each component in the PLGTransformer, we conducted a series of ablation experiments. By retaining the dual-branch framework, we removed specific components to assess their contributions. We used the basic CNN + Swin transformer as the baseline and performed ablation experiments on both datasets for comparison. We separately removed the pyramid self-learning fusion module (PSFM) and the graph neural network fusion module (GFM) on the two datasets. Compared with the baseline dual-encoder model, the full PLGTransformer demonstrated consistent accuracy gains after integrating the PSFM and GFM modules. When the pyramid self-learning fusion module (PSFM) was removed, the model's performance significantly dropped, especially in tasks involving the segmentation of small objects and fine details. For instance, in the Vaihingen dataset, the IoU for the \"car\" category increased from 18.05% in the baseline model to 59.05% in the full model, clearly illustrating PSFM's strong ability in enhancing fine-grained representations and local spatial cues. This demonstrates that shallow feature fusion is crucial for preserving local details such as object shapes and boundaries.When the graph neural network fusion module (GFM) was removed, the model's ability to model global contextual information significantly decreased. Deep feature fusion plays a key role in handling long-range dependencies and spatial continuity in high-resolution RGB imagery. Specifically, the \"tree\" category in the Potsdam dataset showed a marked IoU improvement from 41.01% to 75.14% in the full model, indicating the GFM's effectiveness in modeling spatial relationships and semantic coherence within single-modality RGB features. Figure 9 presents the performance of different modules in the ablation experiments. The Vaihingen dataset contains images with high texture complexity and a significant presence of small objects, such as vehicles, and densely distributed targets, such as buildings. The experimental results, as shown in Table 3, are as follows. After removing the PSFM module, the overall accuracy of the model significantly decreased, with mIoU dropping from 72.85% to 60.04% and mF1 decreasing from 83.80% to 70.62%. This performance drop was particularly noticeable in the recognition of \"small object\" categories. Notably, the car IoU dropped by 29.86%, emphasizing the PSFM's contribution to small-object localization  
Figure 9: Image segmentation results in the ablation study. In this figure, the following are shown: (**1**) original image; (**2**) labels; (**3**) baseline model; (**4**) model with PSFM module only; (**5**) model with GFM module only; (**6**) our proposed model. and edge preservation. The introduction of the PSFM played a key role in multi-scale feature fusion and boundary recovery, especially improving performance in the \"building\" and \"low vegetation\" categories. When the GFM module was removed, the model's performance in handling large-scale scenes declined, with both mIoU and mF1 values dropping. The mIoU decreased from 72.85% to 53.09%, and there was a noticeable blurring of boundaries in large-scale scenes, highlighting the crucial role of GFM in modeling global contextual information. In remote sensing images, land cover objects often exhibit spatial dependencies over long distances. The GFM effectively captures such relationships through topological reasoning over the fused RGB features, without relying on cross-modal fusion. This is particularly evident in categories such as \"tree\", where boundary continuity and spatial consistency are vital for accurate classification. The complete model (base + PSFM + GFM) achieved the best performance on this dataset, with significant improvements over the baseline.