## 3 Results and Discussion
### Comparative Experiment
To validate the generalization ability of the RRMSA-Net model in macroscopic land cover classification tasks, comparative experiments were conducted based on the PASCAL VOC dataset against models such as U-Net [30], the FCN, and SegNet [31], all of which utilize RRG as their backbone network. The experimental results are shown in Table 8. In terms of segmentation accuracy, the MIoU value of the FCN was the lowest, with U-Net and SegNet showing certain improvements over the FCN. The analysis suggests that this is due to the PASCAL VOC dataset including images under various illumination conditions, scales, and occlusions. The comparative models struggle to correctly identify areas obscured by shadows, as they lose detail information during the convolution and pooling processes. Furthermore, the presence of objects at different scales in the images makes it challenging for the models to segment all scale pairs accurately, thereby affecting the segmentation precision of the models. In contrast, the MIoU of RRMSA-Net reached the highest value of 86.35%, attributed to the RRG module and MSAS module enhancing the model's understanding of the underlying semantic information of terrestrial objects in remote sensing images. This result demonstrates the RRMSA-NET model's applicability in processing different geographical areas and land types. The experimental results fully demonstrate the effectiveness of our proposed RRMSA-Net in enhancing the performance of deep neural networks, especially in handling complex semantic segmentation tasks, effectively improving the accuracy and efficiency of the model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **MIoU (\%)** \\ \hline Attention + SSL & RRG & 44.73 \\ CE2P & RRG & 53.10 \\ CNIF & RRG & 56.90 \\ RRMSA-Net (w/MSAS) & RRG & 57.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with state-of-the-art methods on DroneDeploy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **MIoU (\%)** \\ \hline FCN & RRG & 73.25 \\ U-Net & RRG & 76.35 \\ SegNet & RRG & 80.10 \\ RRMSA-Net (w/MSAS) & RRG & 86.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with state-of-the-art methods on PASCAL VOC. To validate the generalization capability of RRMSA-Net, we applied it to the ACGF agricultural dataset, focusing on the extraction of winter wheat distribution. Figure 6A demonstrates the significant advantage of RRMSA-Net in extracting winter wheat, particularly in terms of boundary segmentation. The boundaries of the winter wheat are more distinct and retain finer details, avoiding excessive smoothing of edges. Figure 6B highlights the model's exceptional performance in extracting other crops, with smoother and more precise edge processing. The model accurately identifies the corners of crop fields, showing notable precision. Moreover, the extraction of urban roads is comprehensive, showcasing the model's capability to achieve high segmentation accuracy. Figure 6C illustrates precise urban area extraction, accurately segmenting different sections of the city and clearly delineating the boundaries between roads and their surroundings. This underscores RRMSA-Net's proficiency in recognizing and segmenting edge features in complex urban scenes, further affirming its effectiveness in precise object recognition. In Figure 7A, RRMSA-Net successfully differentiates between other crops and winter wheat, achieving pronounced clarity in boundary delineation. It skillfully identifies urban and barren landscapes amidst winter wheat, accurately outlining their contours and perimeters. This demonstrates its ability to discern and distinguish between different terrain features. In Figure 7B, the model adeptly navigates transitions between roads, urban spaces, and wheat fields, showcasing its precision in road delineation and maintaining segmentation accuracy across natural terrains. Figure 7C introduces urban topographical segmentation, where RRMSA-Net precisely demarcates city areas with smoother boundaries.