## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.2 Dual Large Feature Fusion Selective Kernel Attention Ablation Experiment  
In the previous experiments, the significant potential of integrating the Tok-KAN into the U-Net++ network for remote sensing image segmentation tasks was thoroughly validated. To further explore the potential of the Tok-KAN, this section introduces the DLFF-SKA module on top of the U-Net++ network integrated with the Tok-KAN. The DLFFSKA module was placed between the convolutional operations and the skip connections on the far-left side of the network to examine its impact on improving segmentation performance. To evaluate the effectiveness of the DLFFSKA module compared with the LSK module in remote sensing image segmentation tasks, this study took the U-Net++ network integrated with the Tok-KAN as the baseline model. The LSK and DLFFSKA modules were added to the network, and a comparative experiment was conducted. The results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Method** & **IoU (\%) \(\uparrow\)** & **R (\%) \(\uparrow\)** & **P (\%) \(\uparrow\)** & **F1 (\%) \(\uparrow\)** & **Loss \(\downarrow\)** & **Params (M) \(\downarrow\)** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} DLRSD- \\ Expand \\ \end{tabular} } & Tok-KAN & 72.55 & 82.78 & 83.13 & 82.94 & 0.2995 & 6.28 \\  & Tok-KAN and LSK & 72.77 & 83.26 & 82.93 & 83.10 & 0.2956 & 6.35 \\  & Tok-KAN and DLFFSKA & 76.58 & 85.61 & 85.90 & 85.76 & 0.2498 & 6.38 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Landslide- \\ Expand \\ \end{tabular} } & Tok-KAN & 57.92 & 72.63 & 72.03 & 72.32 & 0.3402 & 6.28 \\  & Tok-KAN and LSK & 58.01 & 71.91 & 72.28 & 72.09 & 0.3395 & 6.35 \\ \cline{1-1}  & Tok-KAN and DLFFSKA & 59.62 & 73.18 & 74.09 & 73.63 & 0.3212 & 6.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between LSK and DLFFSKA modules. Figure 7: The IoU and epoch variation curves of two datasets during the Tok-KAN ablation experiment. Introducing the LSK module to the network resulted in a marginal improvement in the IoU, with increases of 0.22% and 0.09% on the DLRSD-Expand and Landslide-Expand datasets, respectively, compared with the baseline model (Table 7). However, the DLFFSKA module proposed in this study further enhanced the performance by using larger convolution kernels. This modification improved the model's capacity to capture receptive fields at multiple scales, making it more adaptable to the diversity of features in remote sensing images. The DLFFSKA module led to significant improvements in global segmentation performance. As shown in Table 7, despite an increase of just 0.1 M parameters, the DLFFSKA module boosted the IoU by 4.03% and 1.7% on the DLRSD-Expand and Landslide-Expand datasets, respectively, compared with U-Net++ with a Tok-KAN alone, and the loss value decreased by 0.0497 and 0.019, respectively. These results clearly demonstrate the superior performance of the DLFFSKA module in remote sensing image segmentation tasks. Furthermore, to explore the impact of the DLFFSKA module on feature extraction using different fusion kernel volumes, we examined the performance of the module with varying feature fusion convolution weights. The weight parameters \(\alpha\) and \(\beta\) represent the respective weights for feature fusion convolutions with large kernels of seven and nine, as illustrated in Figure 3. The results of this ablation experiment are presented in Table 8.  
When the weight was set to \(\alpha=0.25\) and \(\beta=0.75\), the IoU and F1 values of the two datasets did not change much compared with the LSK module, but the loss value decreased, and the IoU of DLRSD-Expand decreased slightly. This is because the weight of the large kernel convolution was too high, which weakened the model's ability to capture detailed features and affected the overall segmentation performance. When the weight was set to \(\alpha=0.5\) and \(\beta=0.5\), the model achieved better results than the LSK module on both datasets in this paper.