## 3 Results
### Comparison of Different Methods  
For confirming the efficacy of the suggested approach in semantic segmentation, the OA, mIoU, and mF1 indexes after 100 trainings with various classical segmentation algorithms in the three datasets were calculated. The classical semantic segmentation algorithms include the following: Deeplabv3+ (38), Segformer (39), DNLNet (40), Segmenter (41), SegNeXt (42), CMTFNet (29), and SAN (43). For the Vaihingen dataset, the comparisons were made with other advanced algorithms, as exhibited in Table 1. This technique achieved the best mIoU (71.48%), mF1 (81.81%), and OA (90.50%) on the Vaihingen dataset, outperforming other networks. Specifically, the proposed approach not only outperforms the multiscale feature model Deeplabv3+ by 3.5% in terms of mIoU but also outperforms the self-attention-based model, DNLNet, by 3.21%. The segmentation in the background category, in particular, showed a surprising advantage that was far ahead of other methods with IoU. Figure 4 exhibits the semantic segmentation effects of the networks on the Vaihingen dataset. Particularly in the red box, it is evident that the effectiveness of the proposed method is superior to the others, especially in the red box. The ground truth is obviously mislabeled because of a car in the red box. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**IoU**} & \multicolumn{5}{c}{**Indictors**} \\ \cline{2-10}  & **Imp. Surf.** & **Building** & **Low Veg.** & **Tree** & **Car** & **Background** & **mIoU** & **mF1** & **OA** \\ \hline Deeplabv3+ & 84.86 & 90.86 & 71.46 & 80.88 & 59.81 & 14.01 & 66.98 & 76.54 & 89.17 \\ Segformer & 84.11 & 88.28 & 70.53 & 80.51 & 58.30 & 10.38 & 65.35 & 74.92 & 87.97 \\ DNLNet & 84.52 & 90.00 & 70.33 & 80.04 & 60.67 & 24.03 & 68.27 & 78.68 & 89.44 \\ Segmenter & 83.09 & 89.23 & 71.65 & 79.55 & 44.95 & 13.54 & 63.67 & 73.84 & 89.05 \\ SegneXt & 81.09 & 86.24 & 67.50 & 78.22 & 34.27 & 11.52 & 59.81 & 70.38 & 87.40 \\ CMTFNet & 84.36 & 89.68 & 69.79 & 78.79 & 67.21 & 34.82 & 70.78 & 81.41 & 89.89 \\ SAN & 81.77 & 87.34 & 67.53 & 77.63 & 57.09 & 22.64 & 65.67 & 76.81 & 87.60 \\ Proposed & 86.52 & 92.40 & 71.51 & 80.93 & 61.27 & 36.28 & 71.48 & 81.81 & 90.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of the methods on Vaihingen dataset (%). Table 2 presents the comparisons with other techniques on the Potsdam dataset. In comparison to previous networks, the proposed technique produced the mIoU (74.34%), mF1 (83.77%), and OA (88.90%) in the Potsdam dataset. Note that the approach just in the car category earned an IoU of 84.06%, less than 5% lower than the two recent networks, CMTFNet and SAN, resulting in the lower mIoU. It is seen that the OA of the proposed method is the best in Table 2. Figure 5 illustrates how the boundary segmentation detail using the suggested method is more accurate than most of the other networks. The border of the segmented objects in the remote-sensing images can be more accurately depicted about buildings using the suggested way. For the LoveDA dataset, comparative experiments were conducted, further estimating the performance of the presented approach. Table 3 illustrates that the suggested approach can gain superior results on the LoveDA dataset. According to Table 3, the suggested method produced satisfactory results with regard to mIoU (52.57%), mF1 (67.98%), and OA (70.80%). The classification of buildings, roads, and forests shows outstanding advantages. Figure 6 demonstrates the visual results of the LoveDA dataset. It can be observed that a portion of the forest in the red box is identified as water or background in these classical methods. The suggested strategy can significantly enhance the performance, according to the aforementioned experimental results.