## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.1 Tokenized KAN Block Ablation Experiment  
In this section, we evaluate the nonlinear learning capabilities of the Tok-KAN and its effectiveness when integrated with the U-Net++ architecture. Our ablation experiments compare the baseline U-Net++ model with the Tok-KAN-enhanced U-Net++ and the U-KAN model. The comparison focuses on evaluating the performance of these object detection models using three key metrics: the intersection over union (IoU), F1 score, and loss. The experiments were conducted based on the DLRSD-Expand dataset and the expanded Landslide-Expand dataset. In the comparative experiments, we selected the baseline models U-KAN and U-Net++ as well as the Tok-KAN integrated U-Net++ network as test subjects. All models were trained and tested under the same conditions, and a unified set of evaluation metrics was applied for performance assessment. Based on Table 6, the Tok-KAN enhanced the model's ability to capture and represent complex, nonlinear relationships in remote sensing images, particularly in challenging scenarios such as heterogeneous terrain features and landslide regions. As shown in Table 6, the Tok-KAN significantly improved the model's ability to distinguish complex interclass feature relationships on the DLRSD Expand dataset. This capability is essential for handling the diverse natural characteristics found in remote sensing images, where conventional models often struggle to manage the unpredictability of object boundaries and intricate terrain patterns. The Tok-KAN's performance improvement in terms of the IoU (+1.16%) and F1 score (+0.77%) on the DLRSD-Expand dataset highlights its ability to model nonlinear features, outperforming the baseline U-Net++ and U-KAN models. To provide a more intuitive understanding of the Tok-KAN's impact on model performance across different datasets, the training and validation IoU curves (Train IoU and Val IoU) are presented in Figure 7.  
On both the DLRSD-Expand and Landslide-Expand datasets, integrating the Tok-KAN with U-Net++ led to significant improvements in the IoU on the validation set, demonstrating the model's enhanced generalization ability. While the IoU on the training \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} } & U-KAN & 70.33 & 81.60 & 80.78 & 81.19 & 0.3282 & 2.36 \\  & U-Net++ & 71.37 & 82.80 & 81.55 & 82.17 & 0.3146 & 7.07 \\  & U-Net++ & add Tok-KAN & 72.55 & 82.78 & 83.13 & 82.94 & 0.2995 & 6.28 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Landslide- \\ Expand \\ \end{tabular} } & 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 \\ \cline{1-1}  & U-Net++ & add Tok-KAN & 57.92 & 72.63 & 72.03 & 72.32 & 0.3402 & 6.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tok-KAN ablation experiment results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Epoch** \\ \hline DLRSD-Expand & 1000 \\ Landslide-Expand & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model training hyperparameters. set was slightly lower than that of the baseline U-Net++ on the DLRSD-Expand dataset, this suggests that the Tok-KAN's nonlinear modeling capabilities helped the model handle complex interclass relationships, fuzzy boundaries, and interclass interference effectively. The Tok-KAN's regularization effect also alleviated overfitting, improving robustness in real-world scenarios. On the Landslide-Expand dataset, the validation IoU initially fluctuated in the first 100 epochs due to the model's exploratory behavior. However, as training progressed, the Tok-KAN adapted to the complex contextual relationships, leading to steady improvement in the final performance.