## 2 Related Works
### GCN-Based and Other Related Deep Learning Methods  
GCNs [35] perform convolutions in non-Euclidean space, offering a new paradigm for processing data with complex topological structures. In remote sensing segmentation, GCNs can effectively model spatial relationships and contextual dependencies between objects, addressing the limitations of CNNs' local receptive fields. Remote sensing imagery often exhibits complex spatial dependencies. [PERSON] et al. [36] applied GCNs to remote sensing segmentation by constructing pixel-level relational graphs, enhancing spatial correlation modeling--which is particularly beneficial for segmenting irregularly shaped objects. [PERSON] et al. [37] proposed a dynamically constructed GCN model that adaptively connects nodes based on feature similarity, improving segmentation accuracy in complex scenes. [PERSON] et al. [38] innovatively combined GCNs with attention mechanisms to design a spatial-relationship-enhanced network, learning long-range dependencies between objects to significantly improve segmentation performance. [PERSON] et al. [39] further explored multi-scale graph convolution operations, constructing graphs at different scales to improve recognition of objects of varying sizes. In recent years, the integration of CNNs, transformers, and GCNs has become a research hotspot. [PERSON] et al. [40] proposed a hybrid architecture where CNNs extract local features, transformers model global dependencies, and GCNs enhance spatial relationship modeling--together significantly improving remote sensing segmentation accuracy. [PERSON] et al. [41] designed a hierarchical feature fusion framework that applies CNNs, transformers, and GCNs at different levels, achieving more accurate boundary delineation through multi-level feature complementarity. Our proposed PLGTransformer model follows this trend by integrating the strengths of CNNs (local feature extraction), Swin transformers (global context modeling), and GCNs (spatial relationship enhancement) into a unified remote sensing segmentation framework. This model effectively handles multi-scale features and complex textures in remote sensing images while modeling spatial dependencies through graph structures, thus achieving more accurate and robust semantic segmentation results. With the growth of deep learning technologies, research is increasingly focused on improving feature extraction and modeling in remote sensing image segmentation. The Mamba model [42], an emerging deep learning architecture, has shown promise in this area. It combines convolutional neural networks (CNN) with attention mechanisms, enabling effective local feature extraction and global dependency modeling. This allows the Mamba model to distinguish objects in complex images and handle multi-scale features, making it particularly effective for high-resolution remote sensing images with varying object sizes [43]. Additionally, the Mamba model enhances segmentation performance through multimodal information fusion, utilizing data from different sensors such as optical and radar images. This capability improves object recognition and segmentation accuracy in complex environments [44]. Overall, the Mamba model excels in remote sensing image segmentation, leveraging CNNs and attention mechanisms to provide effective solutions across various applications. The YOLO (you only look once) model [45] is a deep-learning-based object detection method recognized for its efficient real-time processing capabilities. Initially designed for object detection, YOLO has gained attention in remote sensing image segmentation due to its end-to-end training, which allows it to learn effective image features automatically. YOLO divides images into grids, with each grid predicting the location and category of objects, enabling the detection and segmentation of multiple targets in a single pass. This approach is more efficient than traditional sliding window techniques, especially for large-area remote sensing images.