## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.4 Ablation Experiment Between Different Methods  
In the preceding three sections, we investigated the insertion position of diverse modules within the network model and the influence of the feature extraction methodology employed within the module on the model performance. In order to further verify the impact of these methods on the overall model performance, this section employs U-Net++ as the baseline network and conducts experiments with the Landslide-Expand dataset. On this basis, the target model was trained separately by combining different methods in different ways to evaluate the specific contribution and interaction of each method. The results of the ablation experiment for different method combinations are presented in Table 10.  
The results of the ablation experiments demonstrate that both the Tok-KAN and DLFFSKA modules, when employed individually, were capable of markedly enhancing the model's performance. When compared with the baseline U-Net++, there was a notable improvement in all evaluation metrics, which served to substantiate the efficacy of the design of these modules. The joint utilization of the Tok-KAN and DLFFSKA (LKN-UKAN) resulted in a further enhancement in performance when compared with the use of these two modules in isolation. As shown in Table 10, despite a reduction in the model parameters by 0.79 M, there was an improvement in the IoU, recall, precision, and F1 scores by 1.74%, 1.16%, 1.83%, and 1.49%, respectively. Furthermore, the loss was reduced by 0.0176 in comparison with the baseline U-Net++. This demonstrates that the nonlinear properties of the Tok-KAN and the contextual understanding capability of DLFFSKA exhibited a high degree of complementarity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **IoU (\%) \(\uparrow\)** & **R (\%) \(\uparrow\)** & **P (\%) \(\uparrow\)** & **F1 (\%) \(\uparrow\)** & **Loss \(\downarrow\)** & **Params (M) \(\downarrow\)** \\ \hline U-Net++ & 57.88 & 72.02 & 72.26 & 72.14 & 0.3388 & 7.07 \\ U-Net++ add Tok-KAN & 57.92 & 72.63 & 72.03 & 72.32 & 0.3402 & 6.28 \\ U-Net++ add DLFFSKA & 58.19 & 73.49 & 71.36 & 72.41 & 0.3407 & 7.17 \\ U-Net++ add Tok-KAN and DLFFSKA & 59.62 & 73.18 & 74.09 & 73.63 & 0.3212 & 6.38 \\ 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 10: Comparison of different feature weights. Figure 9: The IoU and epoch variation curves of the target dataset during the FTTL ablation experiment. Nevertheless, for the Landslide-Expand dataset, which has a single target category and intricate, detailed features, it remained challenging to fully harness the model's potential by relying solely on the aforementioned modules. Accordingly, we introduced the FTTL strategy with the objective of enhancing the model's performance. In comparison with the baseline U-Net++, the utilization of FTTL alone resulted in improvement in the IoU, recall, precision, and F1 score by 6.98%, 4.27%, 5.1%, and 4.68%, respectively, accompanied by a reduction in loss of 0.3045. In combination with the Tok-KAN and DLFFSKA, the IoU, recall, precision, and F1 score were improved by 12.12%, 6.01%, 10.99%, and 8.42%, respectively, and the loss was reduced by 0.3105 in comparison with the baseline U-Net++. This provides compelling evidence in favor of the joint method. In order to illustrate the impact of each method on the model's predictive efficacy, three images were selected for display in Figure 10. These images are accompanied by the heatmaps and segmentation results (Mask) of the prediction results, which were generated through the execution of ablation experiments utilizing a variety of methods. The leftmost column (Image and GT) depicts the original images and ground-truth masks of the three groups of images in sequence.