Multi-Scale Feature Fusion and Global Context Modeling for Fine-Grained Remote Sensing Image Segmentation  
[PERSON]  
1  
[PERSON]  
1  
Footnote 1: email: {[[https://www.sci.edu.cn/~sri](https://www.sci.edu.cn/~sri)]([https://www.sci.edu.cn/~sri](https://www.sci.edu.cn/~sri))  
The synergistic fusion of multisource remote sensing data establishes a new paradigm for Earth system science research. By integrating complementary sensor advantages, researchers can construct multidimensional feature spaces to enhance surface characterization capabilities. This paradigm demonstrates remarkable efficacy in change detection: SAR's all-weather observation combined with optical spectral information enables precise urban expansion monitoring [1; 3; 4]; hyperspectral-LiDAR fusion elevates land cover classification accuracy beyond 90% [6; 7]; and multi-temporal multi-angle image integration effectively resolves camouflage recognition challenges in military applications [8; 9]. These breakthroughs propel the paradigm shift from visual to intelligent interpretation in remote sensing [10; 11; 12; 13]. As the core technology for deep image understanding, semantic segmentation has gained significant attention in remote sensing. Through pixel-level classification generating semantic thematic maps, it supports decision making in digital twin cities, flood assessment, and precision agriculture. However, unique remote sensing characteristics--sub-pixel features (e.g., small-scale structures), inter-class similarity (e.g., vegetation types), and target occlusion--pose formidable challenges to conventional segmentation algorithms [14; 15; 16; 17]. Particularly in high-density urban areas, traditional CNNs (i.e., convolutional neural networks) struggle to distinguish occluded boundaries due to limited receptive fields, leading to significant accuracy degradation. These challenges drive research on novel segmentation frameworks tailored for remote sensing characteristics. While CNN-based models excel in feature extraction, their hierarchical downsampling mechanisms progressively discard small-scale features. For instance, 3- to 5-pixel-wide road markings in high-resolution urban imagery degrade to sub-pixel levels after four downsampling operations, complicating network recognition. Furthermore, weakened inter-class heterogeneity (e.g., vegetation spectral confusion, similar reflectance between buildings and pavements) undermines the CNN's reliance on local textures for establishing discriminative boundaries [15; 18; 19; 20]. Combined with vertical occlusion effects, these limitations exacerbate pixel-level semantic ambiguity, highlighting the inadequacy of single-scale feature extraction. To tackle these challenges, we focus on developing multi-granularity feature fusion systems that combine local details with global context. The Swin transformer [21] introduces hierarchical window attention mechanisms alongside CNN-style feature learning. It features: (1) progressive feature pyramids achieved through patch merging, (2) window-based multi-head self-attention (W-MSA) that reduces computational complexity, and (3) shifted window (SW-MSA) mechanisms that address limitations in the visual field. However, the limitations of transformers in weak boundaries (such as water-wetland transitions) and high-frequency textures (like farmland furrows) highlight the need for the local inductive bias of CNNs, which has prompted research into hybrid architectures [22]. Dual-branch networks that combine CNN and transformer pathways, along with channel-spatial attention mechanisms, show superior multimodal fusion capabilities. Building on these foundations, this paper proposes PLGTransformer--a dual-encoder-enhanced hybrid architecture that innovatively integrates local perception, global modeling, and graph-structured reasoning. Compared with existing hybrid CNN-transformer architectures such as Swin UNet and FTUNetFormer, PLGTransformer introduces several technical and structural innovations.