## 3 Results and Discussion
### Comparative Experiments Results and Discussion  
In order to further verify the superiority of the network designed in this paper for landslide image segmentation, some mainstream segmentation models were selected for comparative experiments, including PSPNet, Deephlabv3+, U-Net, U-KAN, U-Net++, and the LKN-UKAN method using FTTL in this paper. The results of the comparative experiments are shown in Table 11, and the comparison results are shown in Figure 11.  
As demonstrated in Table 11, for the algorithm comparison experiments, although the U-KAN [45] exhibited the lowest parameter count among all networks, its segmentation performance did not demonstrate corresponding superiority. The conventional segmentation architectures, including PSPNet [38], Deeplabv3+ [39], U-Net [31], and U-Net++ [32],showed no significant advantages across any metrics on our dataset. Notably, while the transfer learning approaches TransLandSeg [47] and U-Net++ with FTTL achieved better segmentation performance than traditional methods, ours (LKN-UKAN with FTTL) demonstrated superior comprehensive performance. Specifically, compared with these transfer learning-enhanced methods, our approach achieved optimal performance across multiple evaluation metrics while maintaining a marginal increase in parameter count. As clearly evidenced in Table 11, quantitative analysis revealed that the proposed method achieved significant improvements of 12.22% for the IoU, 5.4% for precision, and 8.36% for the F1 score compared with the baseline network U-Net++ used in this study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **IoU (\%) \(\uparrow\)** & **R (\%) \(\uparrow\)** & **P (\%) \(\uparrow\)** & **F1 (\%) \(\uparrow\)** & **Loss \(\downarrow\)** & **Params (M) \(\downarrow\)** \\ \hline PSPNet & 30.78 & 34.82 & 69.13 & 46.31 & 0.0608 & 16.67 \\ Deeplabv3+ & 47.47 & 63.87 & 66.36 & 65.09 & 0.2415 & 13.47 \\ U-Net & 46.35 & 62.76 & 62.37 & 62.56 & 0.4825 & 3.12 \\ U-KAN & 57.78 & 72.63 & 71.78 & 72.20 & 0.3402 & 2.36 \\ U-Net++ & 57.88 & 72.02 & 72.26 & 72.14 & 0.3388 & 7.07 \\ TransLandSeg & 62.97 & 73.96 & 77.27 & 75.58 & 0.3107 & 4.18 \\ U-Net++ with FTTL & 64.86 & 76.29 & 77.36 & 76.82 & 0.0343 & 7.07 \\ Ours & 70.00 & 78.03 & 83.25 & 80.56 & 0.0283 & 6.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of different feature weights. Figure 10: Visualization results of ablation experiments using different methods. E1 = U-Net++; E2 = U-Net++ add Tok-KAN; E3 = U-Net++ add DLFFSKA; E4 = U-Net++ add Tok-KAN and DLFFSKA (LAN-UKAN); E5 = U-Net++ with FTTL; **Ours = LAN-UKAN with FTTL**. As demonstrated in Figure 11a,b, conventional segmentation architectures (PSPNet, DeepLabV3+, U-Net, U-KAN, and U-Net++) exhibited fundamental capability in delineating primary landslide areas within concentrated landslide regions. However, these methods demonstrated limited precision in edge detection, particularly failing to distinguish discrete landslide patches adjacent to main landslide bodies. While transfer learning-enhanced approaches (TransLandSeg and U-Net++ with FTTL) showed improved boundary recognition, their performance remained suboptimal for internal non-landslide features within landslide areas. In contrast, our proposed method (LKN-UKAN with FTTL) achieved superior edge delineation accuracy while effectively identifying both intra-landslide heterogeneities and peripheral scattered landslide clusters, demonstrating enhanced granularity in segmentation outcomes. Figure 11b,c reveals the critical limitations of conventional methods in handling landslide discontinuities, where these architectures consistently misclassified fracture zones as landslide areas. Ours emerged as the sole framework capable of accurate fracture detection while maintaining high sensitivity to marginal landslide features in image peripheries, confirming its exceptional segmentation capability.