## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.2 Dual Large Feature Fusion Selective Kernel Attention Ablation Experiment
To verify that the smaller the proportion of large kernel convolutions, the better the effect, we set the weight to \(\alpha=0.75\) and \(\beta=0.25\). Compared with the LSK module, it achieved some improvement, but it was still far lower than when the weight was set to \(\alpha=0.5\) and \(\beta=0.5\). When the weight of the DLFFSKA feature fusion kernel was set to \(\alpha=0.5\) and \(\beta=0.5\), the model on the DLRSD-Expand dataset improved the IoU and F1 score by 3.81% and 2.66% compared with the LSK module, respectively, and the loss value decreased by 0.0458. On Landslide-Expand, the IoU and F1 score improved by 1.61% and 1.54% compared with the LSK module, and the loss value decreased by 0.0183. The heatmaps presented in Figure 8 provide an intuitive visualization of the performance improvements achieved by the DLFFSKA attention mechanism. The color intensity reflects the confidence levels, with deep red indicating high confidence and deep blue indicating low confidence. The transition zones between red and blue represent intermediate values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **IoU (\%) \(\uparrow\)** & **F1 (\%) \(\uparrow\)** & **Loss \(\downarrow\)** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} DLRSD- \\ Expand \\ \end{tabular} } & add LSK & 72.77 & 83.10 & 0.2956 \\  & add DLFFSKA (\(\alpha=0.25\), \(\beta=0.75\)) & 72.69 & 83.11 & 0.2972 \\  & add DLFFSKA (\(\alpha=0.5\), \(\beta=0.5\)) & 76.58 & 85.76 & 0.2498 \\  & add DLFFSKA (\(\alpha=0.75\), \(\beta=0.25\)) & 73.99 & 83.94 & 0.2797 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Landslide- \\ Expand \\ \end{tabular} } & add LSK & 58.01 & 72.09 & 0.3395 \\  & add DLFFSKA (\(\alpha=0.25\), \(\beta=0.75\)) & 58.09 & 72.33 & 0.3406 \\  & add DLFFSKA (\(\alpha=0.5\), \(\beta=0.5\)) & 59.62 & 73.63 & 0.3212 \\  & add DLFFSKA (\(\alpha=0.75\), \(\beta=0.25\)) & 59.47 & 73.43 & 0.3233 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of different feature weights. The analysis revealed that without the attention mechanism, the target regions were ambiguous, with numerous red-to-blue transition areas in the heatmap (Figure 8A), leading to significant misidentification of boundaries. When the LSK attention mechanism was added (Figure 8B), these transition areas were reduced, and the target boundaries became clearer. However, for the smaller target regions, the model still struggled with incomplete recognition. With the introduction of the DLFFSKA attention mechanism, the transition zones between the target boundaries and the background were further minimized (Figure 8C). As the ratio of the large kernel weight increased, the distinction between high- and low-confidence regions became more pronounced. The \(\alpha=0.5\) and \(\beta=0.5\) configuration yielded the best performance (Figure 8D), clearly distinguishing the target-background boundary and accurately identifying the target region with high confidence. The results effectively demonstrate that the DLFFSKA mechanism, by balancing large and small convolution kernels, significantly enhanced the model's ability to perceive global features while retaining image details. This improvement optimizes the model's performance in recognizing remote sensing image features, resulting in a clearer delineation between targets and backgrounds and better identification of target areas. Here, U-Net++ with the addition of a Tok-KAN and DLFFSKA (\(\alpha=0.5\) and \(\beta=0.5\)) is referred to as the Large Kernel Nested UKAN (LKN-UKAN).