## 3 Results and Discussion
### Comparative Experiment
All the compared models utilize ResNet-101 as the backbone network, and the experimental results are presented in Table 6. In terms of segmentation accuracy, RefineNet exhibited the lowest MIoU value, while SGR and ACNet showed marginal improvements compared to RefineNet. The analysis revealed that the macro-geographical nature of the LandCoverNet dataset poses a challenge, indicating limitations in the generalization performance of the compared models across varying geographical environments. Additionally, the dataset employed 10 m resolution remote sensing images, which cannot offer precise pixel information akin to other high-resolution remote sensing images. This might have posed difficulties for the comparison models in identifying and extracting fine details from the images, thereby affecting segmentation accuracy. In comparison to the other three models, RRMSA-Net achieved a superior MIoU value of 45.21%. This is due to the enhanced understanding of the underlying semantic information of target features in remote sensing imagery provided by the RRG and MSAS modules. The results underscore the versatility of the RRMSA-NET model in handling diverse geographical regions and land types. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **MIoU (\%)** \\ \hline RefineNet & ResNet-101 & 40.20 \\ SGR & ResNet-101 & 44.30 \\ ACNet & ResNet-101 & 45.06 \\ RRMSA-Net (w/MSAS) & RRG & 45.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with state-of-the-art methods on LandCoverNet. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Weights (m)** & **FLOPs (b)** & **MIoU (\%)** & **Acc (\%)** \\ \hline ResNet-50 & 23.7 & 4.1 & 75.3 & 92.2 \\ Thinet-ResNet-50 & 15.4 & 2.6 & 72.1 & 90.3 \\ NISP-ResNet-50-B & 14.2 & 2.2 & - & 90.8 \\ Versatile-ResNet-50 & 11.1 & 3.1 & 74.5 & 91.7 \\ SSS-ResNet-50 & - & 2.9 & 74.2 & 91.8 \\ MSAS-ResNet-50 (s = 2) & 12.9 & 2.4 & 75.0 & 92.3 \\ \hline Shift-ResNet-50 & 6.2 & - & 70.6 & 90.1 \\ Taylor-FO-BNF-ResNet-50 & 7.8 & 1.4 & 71.7 & - \\ Slimmable-ResNet-50 & 0.5\(\times\) & 6.5 & 1.2 & 72.1 & - \\ MetaPruning-ResNet-50 & - & 1.1 & 73.4 & - \\ RRG-ResNet-50 (s = 4) & 6.2 & 1.0 & 74.1 & 92.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of state-of-the-art methods for compressing RRG on PASCAL VOC dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **MIoU (\%)** \\ \hline PSPNet & MobileNet & 78.4 \\ SeNet & MobileNet & 81.2 \\ HRNet & RRG & 81.6 \\ RRMSA-Net (w/MSAS) & RRG & 83.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with state-of-the-art methods on LoveDA. In Table 7, to assess the capability of the RRMSA-Net model in processing unmanned aerial vehicle (UAV) imagery tasks, we conducted comparative experiments using the DroneDeploy dataset against models such as Attention+SSL [27], CE2P [28], and CNIF [29], all of which incorporate the RRG module as their backbone network. The results are presented in Table 3. In terms of segmentation accuracy, the model with Attention + SSL applied to PSPNet recorded the lowest Mean Intersection over Union (MIoU) value, followed by CE2P and CNIF. This outcome can be attributed to the susceptibility of UAV imagery to lighting conditions; shadows can obscure ground features, complicating the identification of areas concealed by shadows for the comparative models. Additionally, Attention + SSL, CE2P, and CNIF did not effectively retain sufficient detail when extracting higher level abstract features, failing to achieve a balance between global context and local detail information, which resulted in lower segmentation accuracy. In contrast, RRMSA-Net achieved the best performance with an MIoU value of 57.39%, thanks to the MSAS module's employment of a sparse self-attention mechanism, which enhanced the model's focus on target features, thereby improving segmentation accuracy. These findings underscore the RRMSA-Net model's utility and robustness in handling UAV aerial imagery.