## 5 Conclusions  
In this paper, we proposed MSCSA-Net for improving semantic segmentation of remote sensing imagery. MSCSA-Net uses feature split and feature fusion from different scales twice. Specifically, three attention modules were proposed to enhance the semantic information of the features before and after fusion. The first attention module is the LCSA module, which is the attention on the experimental results and can effectively enhance the scene features The second attention module is the MSA module, which can reduce \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Impervious** & \multirow{2}{*}{**Buildings (F1)**} & **Low Vegetation** & \multirow{2}{*}{**Trees (F1)**} & \multirow{2}{*}{**Cars (F1)**} & \multirow{2}{*}{**m-F1**} & \multirow{2}{*}{**mIoU**} \\  & **Surfaces (F1)** & & & & & & & \\ \hline ResU-net (baseline) [16] & 72.1 & 76.0 & 53.9 & 67.3 & 39.4 & 67.1 & 50.5 \\ FCN-8s [20] & 71.3 & 71.6 & 63.7 & 65.8 & 44.7 & 77.1 & 63.4 \\ SegNet [9] & 75.7 & 88.3 & 62.8 & 77.2 & 37.2 & 77.0 & 64.1 \\ CBAM [29] & 78.4 & 80.3 & 67.0 & 68.9 & 52.5 & 81.6 & 69.5 \\ RefineNet [19] & 77.4 & 86.9 & 78.3 & 79.6 & 61.2 & 81.6 & 69.2 \\ Deeplabv3+ [14] & 79.4 & 90.0 & 66.9 & 80.1 & 52.6 & 81.5 & 69.5 \\ SCAttNet [21] & 80.4 & 90.3 \({}^{\dagger}\) & **80.0** & 80.3 & 54.4 & 81.1 & 70.2 \({}^{\dagger}\) \\ MSCSA-Net (\(d=4\)) & 85.4 \({}^{\dagger}\) & 90.2 & 72.3 & 82.5 \({}^{\dagger}\) & **62.5** & **82.4 \({}^{\dagger}\)** & 70.1 \\ MSCSA-Net (\(d=1\)) & **85.7** & **90.5** & 74.2 \({}^{\dagger}\) & **82.7** & 61.8 \({}^{\dagger}\) & **83.0** & **70.9** \\ \hline \hline \end{tabular} Bold means the best, \({}^{\dagger}\) means the second best.  
\end{table} 
Table 4: Comparison using the Vaihingen dataset. The F1-score was used as the evaluation metric for each category. The overall results were evaluated using the m-F1-score and mIoU. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Impervious** & \multirow{2}{*}{**Buildings (F1)**} & \multirow{2}{*}{**Low Vegetation** & \multirow{2}{*}{**Trees (F1)**} & \multirow{2}{*}{**Cars (F1)**} & \multirow{2}{*}{**m-F1**} & \multirow{2}{*}{**mIoU**} \\  & **Surfaces (F1)** & & & & & & & \\ \hline ResU-net (baseline) [16] & 70.8 & 67.3 & 53.7 & 48.6 & 22.9 & 63.6 & 46.7 \\ FCN-8s [20] & 66.8 & 74.2 & 57.2 & 51.6 & 69.0 & 68.5 & 55.3 \\ SegNet [9] & 68.2 & 76.1 & 63.9 & 58.5 & 75.0 & 71.9 & 59.4 \\ CBAM [29] & 79.5 & 87.0 & 69.0 & 65.1 & 79.9 & 77.1 & 66.6 \\ RefineNet [19] & 77.9 & 79.4 & 69.4 & 65.4 & 78.4 & 75.5 & 64.5 \\ Deeplabv3+ [14] & 80.7 & 86.6 & 71.5 & 64.5 & 79.0 & 77.2 & 66.8 \\ SCAttNet [21] & 81.8 & 88.8 & 72.5 & 66.3 & 80.3 & 74.8 & 68.3 \\ MSCSA-Net (\(d=4\)) & **87.0** & 89.1 \({}^{\dagger}\) & 73.3 \({}^{\dagger}\) & **75.2** & **80.8** & 83.2 \({}^{\dagger}\) & 71.2 \({}^{\dagger}\) \\ MSCSA-Net (\(d=1\)) & 86.9 & **90.0** & **74.3** & 74.2 \({}^{\dagger}\) & 80.6 \({}^{\dagger}\) & **83.7** & **71.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison using the Potsdam dataset. The F1-score was used as the evaluation metric for each category. The overall results were evaluated using the m-F1-score and mIoU. the number of parameters through the adjustment of the channel reduction rate d, which mainly helps the model perform more accurate segmentation of small objects in the RSI semantic segmentation task. The third attention module is the 16-LCSA module, which uses 16 split local features for residual SE and residual CAM operations to help the model perform more local feature enhancement on the feature map and strengthen the scene prediction of the feature map. The LCSA module with local attention is beneficial for the classification of confusing regions, while the MSA module can use different receptive fields to enhance the representation of global features. Experimental results with two benchmark RSI datasets, the ISPRS Vaihingen and Potsdam datasets, showed that the proposed method greatly improved the representation of extracted features.