## 4 Experiments and Results
### Comparative Experiment and Results
Each column (**a–g**) represents a different scene, and each row represents a different model: (**a**) roads and shade houses in complex backgrounds; (**b**) shade houses and dark-colored water bodies; (**c**) shade houses and dark-colored vegetation; (**d**) shade houses and similar objects (e.g., black roofs and shade nets); (**e**) shade houses and dark lands, black plastic films; (**f**) high-density distribution of shade houses and warehouses; (**g**) shade houses in complex shadowed scenes. subtle differences. Although Segformer [46] show improvement in global feature capture, they still fall short of ShadeNet's dual attention mechanism in terms of detail differentiation. In the scenario shown in Figure 13d with similar objects such as black roofs and shade nets, ShadeNet, through dynamically weighted channel and spatial features, significantly improves its ability to perceive local features and global relationships in complex backgrounds, ensuring the accuracy of shade houses region segmentation and the integrity of boundaries. While Segformer [46], based on the self-attention mechanism, shows a significant improvement in accuracy compared to traditional CNN networks, it still encounters issues such as misclassifying black roofs as shade houses. In the scenario shown in Figure 13e, ShadeNet effectively distinguishes shade houses from dark lands and black plastic films, significantly reducing misclassification and improving the segmentation quality of target boundaries, thanks to its outstanding global perception and local feature capture abilities. While Segformer [46] performs well in capturing global information in similar scenarios, it still faces issues with blurring and misjudment in detail areas, failing to completely avoid misclassification. In the scenario shown in Figure 13f, where shade houses and warehouses are densely distributed, other models often confuse background and target regions, while ShadeNet's self-attention mechanism and spatial-channel attention mechanism balance the target's detailed features and contextual information, significantly reducing the misclassification rate. U-Net [27] and HRNet [28] perform especially poorly in such high-density similar object distribution scenarios, with a higher misclassification rate and an inability to effectively distinguish tightly distributed similar objects. Finally, in the complex shadowed scene shown in Figure 13g, ShadeNet's channel-spatial attention mechanism demonstrates excellent semantic extraction ability, accurately identifying the obscured shade houses regions and ensuring the completeness and accuracy of the segmentation results. In contrast, Segformer [46] perform poorly in the shadow-covered area, often missing the occluded targets, resulting in incomplete segmentation outcomes. In summary, the ShadeNet significantly enhances its ability to discriminate complex backgrounds and similar objects by integrating self-attention and GCLSA mechanisms. The self-attention mechanism empowers the model with strong global perceptive capabilities, enabling it to effectively capture long-range dependencies and extract global features. On the other hand, the GCLSA mechanism strengthens the model's focus on key regions and detailed information by dynamically adjusting the channel and spatial weights of the feature maps. The synergistic effect of these dual attention mechanisms allows ShadeNet to strike a better balance between multi-level feature fusion, detail processing, and global understanding, thereby maintaining high performance across various complex and dynamic remote sensing image segmentation tasks. The experimental results demonstrate that ShadeNet exhibits remarkable robustness and reliability in both complex scenes with significant visual similarities (e.g., dark land and black PMFs) and in scenes with occlusions and background interference (e.g., PCGs under shadow coverage). Its semantic segmentation performance significantly outperforms other mainstream models.