## 3 Results and Discussion
### Comparative Experiment  
In our latest research, we integrated the Rapid Redundant Feature-Generation module and the multi-level spatial aggregation strategy into the existing architecture of DeepLabv3+, with the aim of creating a more compact and efficient model. Comprehensive experiments were conducted on the LoveDA, LandCoverNet, and PASCAL-VOC datasets. According to the data presented in Table 4, our RRMSA-Net model achieved approximately twice the computational acceleration and model compression while maintaining the accuracy of the original DeepLabv3+ model architecture. Compared with the latest advanced methods including Thinet [19], NISP [20], Versatile filters [21], and Sparse Structure Selection (SSS) [22], our method exhibited significantly superior performance under a two-\(\times\) acceleration condition. When we further increased the hyperparameter s to four, the RRMSA-Net model experienced only a 0.3% minor drop in accuracy, while gaining about a four-\(\times\) increase in computational speed, demonstrating even higher performance efficiency. This study not only validates the effectiveness of RRMSA-Net in constructing efficient deep neural networks, but also proves its ability to maintain outstanding performance in processing large-scale image-recognition tasks, even under resource-constrained conditions. To validate the RRMSA-Net model's capabilities in domain adaptation and handling complex backgrounds, we conducted comparative experiments on the LoveDA dataset against the PSPNet, SeNet, and HRNet models, all of which utilize the lightweight MobileNetV2 as their backbone network. The results, presented in Table 5, indicate segmentation accuracy with the PSPNet model recording the lowest Mean Intersection over Union (MIoU) value, followed by SeNet and HRNet [23]. This outcome suggests a shortfall in the feature-extraction capabilities of PSPNet, SeNet, and HRNet, particularly in distinguishing between urban and rural scene features within the LoveDA dataset. Rural scenes are predominantly composed of a few man-made features, like buildings and roads, alongside a vast number of natural objects, such as woodlands. Urban scenes, conversely, feature a blend of buildings and roads, with fewer natural elements. This variance in sample distribution and scale diversity poses challenges for the compared models in accurately classifying land objects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(P_{h}\) & \(P_{w}\) & **Pixel Acc (\%)** & **MIoU (\%)** \\ \hline Dilated MSAS & - & - & 96.08 & 75.30 \\  & 4 & 4 & 96.30 & 78.97 \\  & 4 & 8 & 96.32 & 78.94 \\  & 8 & 4 & 96.33 & 79.20 \\ +MSAS-OC & 8 & 8 & 97.82 & 80.62 \\  & 8 & 16 & 96.19 & 79.01 \\  & 16 & 8 & 96.32 & 78.02 \\  & 16 & 16 & 96.31 & 79.40 \\ \hline +MSAS-OC & 8 & 8 & 96.26 & 79.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of \(P_{h}\) and \(P_{w}\), the order of global relation and local relation within the interlaced sparse self-attention on LOveDA. RRMSA-Net enhances feature representation through the RRG module, utilizing the Expand Spatial Feature Overlay (ESFO) strategy and the Boundary Definition and Management (BQAM) mechanism to highlight critical features. Concurrently, the Multi-Scale Aggregation Scheme (MSAS) module improves the model's grasp on contextual semantics by aggregating neighboring pixels. This comprehensive approach propelled the RRMSA-Net's MIoU value to 91.3%. These findings underscore RRMSA-Net's superior performance in processing remote sensing images with pronounced urban-rural contrasts, showcasing its strengths in multi-scale target recognition and domain adaptation. In Table 6, to verify the generalization ability of the RRMSA-Net model in macro land cover classification tasks, we performed a comparative experiment using the LandCoverNet dataset with the RefineNet [24], SGR [25], and ACNet [26] models.