## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.3 Fine-Tuning Transfer Learning Ablation Experiment  
The preliminary results show that the LKN-UKAN achieved notable performance improvements on the feature-rich DLRSD-Expand dataset. However, performance gains on the Landslide-Expand dataset, characterized by a single category and complex features, were relatively limited. To enhance model performance on the Landslide-Expand dataset, we explored the use of FTTL in this section. Figure 8: Heatmap of DLFFSKA ablation experiment. Image = orgin image; GT = ground truth. (**A**) Add Tok-KAN to U-Net++. (**B**) Add Tok-KAN and LSK to U-Net++. (**C**) Add Tok-KAN and DLFFSKA (\(\alpha=0.25\), \(\beta=0.75\)) to U-Net++. (**D**) Add Tok-KAN and DLFFSKA (\(\alpha=0.5\), \(\beta=0.5\)) to U-Net++. (**E**) Add Tok-KAN and DLFFSKA (\(\alpha=0.75\), \(\beta=0.25\)) to U-Net++. FTTL offers the advantage of selectively freezing specific network layers, thereby improving efficiency and adaptability to different dataset characteristics. In our experiments, we used the pretrained LKN-UKAN model on the DLRSD-Expand dataset as the baseline, sequentially freezing different layers during fine-tuning on the Landslide-Expand dataset to evaluate the impact of layer freezing on model performance. As shown in Table 9, when no layers were frozen, the model's performance improved significantly, with the IoU, recall, precision, and F1 scores increasing by 10.38%, 4.85%, 9.16%, and 6.93%, respectively, compared with the baseline model, while the loss value decreased by 0.2929. Furthermore, the average training time per batch was reduced by 9 s, indicating that FTTL not only enhanced model performance but also optimized training efficiency. This aligns with existing research indicating that fine-tuning enables more efficient adaptation to target datasets. However, performance degraded when freezing low-level layers, suggesting that the pretrained features from the DLRSD-Expand dataset may not have adequately captured the unique isomorphic features of the Landslide-Expand dataset. Specifically, freezing low-level layers reduced the model's ability to dynamically adjust the mid- and high-level features, which is crucial for distinguishing the fine terrain and structural details of landslide images. This result is consistent with previous studies showing that low-level features, such as textures and edges, are often too general and require fine-tuning for better adaptation to specialized tasks. Further analysis revealed that freezing the layers resulted in faster training times but did not improve model accuracy. When the low-level layers were frozen, the model's capacity to capture local variations was compromised, leading to a decline in segmentation performance. This highlights the critical role of low-level feature fine-tuning in effectively transferring knowledge from a rich base dataset to a specialized target dataset. Figure 9 shows the variation in the IoU and loss values across different freezing strategies. The model using FTTL without freezing the layers outperformed all others, demonstrating superior IoU and lower loss values. As more layers were frozen, both the IoU and loss decreased, supporting the hypothesis that non-frozen layers provided better feature representation for the Landslide-Expand dataset. In conclusion, in the landslide image segmentation task, the strategy of not freezing any network layers when using FTTL can significantly improve the performance of the target model while taking into account the training efficiency. The strategy of freezing layers has certain advantages in terms of computational efficiency, but it performed worse than the fully unfrozen network model in terms of geomorphic feature expression and segmentation accuracy.