## 4 Experiments and Discussion
### Discussion  
PLGTransformer integrates the advantages of convolutional neural networks (CNNs) and vision transformers, achieving outstanding performance in remote sensing image segmentation tasks. Specifically, the CNN backbone is responsible for capturing local fine-grained features, while the transformer module models global contextual dependencies and long-range interactions. This multi-level feature fusion mechanism enables PLGTransformer to effectively handle complex scenes and objects with rich structural details.  
Although PLGTransformer demonstrates excellent accuracy in remote sensing segmentation, it still faces challenges in distinguishing certain categories, particularly trees and low vegetation. These classes often exhibit high similarity in color, texture, and shape, with blurred boundaries, posing difficulties for fine-grained discrimination. Currently, the model mainly relies on adaptive graph fusion and boundary-aware loss for edge modeling, without explicitly incorporating traditional edge detection algorithms like Canny, Sobel, or post-processing methods like conditional random fields (CRFs). In future work, we plan to investigate integrating classical edge detection results as auxiliary supervision signals, or incorporating lightweight post-processing modules as plugins to enhance boundary discrimination.  
Moreover, despite its superior segmentation accuracy, PLGTransformer has relatively high computational complexity, particularly when processing high-resolution images. Memory consumption and inference time are considerably longer. Thus, optimizing the architecture to reduce computational and memory costs remains an important direction. Future research may delve into model compression for real-time applications, quantization techniques, or more efficient attention mechanisms, as well as lightweight models tailored for large-scale data processing.  
Figure 11: The Grad-CAM visualization of different classes of features: (**1**) original image; (**2**) impervious surface; (**3**) building; (**4**) car; (**5**) low vegetation; (**6**) tree.  
Multimodal data fusion remains a vital area of research in remote sensing semantic segmentation. Although existing studies that combine multiple data types, such as optical imagery and DSM, have demonstrated improved separability for certain land cover categories, they also highlight challenges, including modality heterogeneity, spatial misalignment, and inconsistent feature representations [54; 55; 56; 57]. In future work, we aim to incorporate diverse remote sensing modalities into the PLGTransformer framework, and design more adaptive cross-modal fusion mechanisms to mitigate the aforementioned issues and enhance model generalizability and robustness in complex scenarios involving SAR or LiDAR.  
Finally, the design philosophy of PLGTransformer also holds potential for applications in fields such as medical image analysis and autonomous driving, where segmentation tasks often involve complex boundaries and multi-scale structures. However, the characteristics of data and task requirements differ significantly across domains, and thus model structure and training strategies need to be specifically adapted. Future studies will explore these directions to achieve effective cross-domain deployment and generalization.