## 5 Results
### Ablation Study  
To determine the optimal hyperparameters for training, we conducted ablation experiments on the momentum and weight decay values in the training method, with the experimental results illustrated in Figure 6. The results demonstrate that UniU-Net achieves a significant advantage in the segmentation of betel nut trees when the momentum value is between 0.8 and 0.9. Additionally, a weight decay value of 0.001 yields superior overall training performance compared to 0.0001. Consequently, this study selects a momentum value of 0.9 and weight decay of 0.001 as the hyperparameters for the SGD optimizer during training. Taking into account the spectral characteristics of area palm trees and research in the field of quantitative remote sensing, this study calculated the DVI, RVI, and MSAVI vegetation indices as additional spectral features for the initial data and conducted ablation experiments on the model performance. The experimental results are presented in Table 5. When using only the spectral features of the vegetation indices as input data, the overall segmentation performance of the model was relatively poor. This was attributed to the minimal absolute quantity of spectral features, which provided limited learning features for the model. However, after integrating the spectral features of the vegetation indices with the original data, UniU-Net showed slight improvements in both the F1 and IoU metrics. This enhancement is a result of the additional feature information provided by the spectral features of the vegetation indices. However, the extent of the performance improvement was not significant. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Param. (M)** & **Speed (FPS)** \\ \hline ResU-Net & 23.1 & **55** \\ MAReU-Net & 26.3 & 33 \\ ABCNet & 13.5 & 48 \\ BANet & 12.7 & 41 \\ CREVNet & **1.3** & 49 \\ LiViTNet & 6.8 & 39 \\ UniU-Net (ours) & 33.5 & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of parameters and inference speeds of different methods. Figure 6: Comparison of results regarding hyperparameter settings. To evaluate the performance gain due to UniU-Net's improved design components, we conducted a series of ablation experiments. These experiments primarily focused on examining how the auxiliary encoder and the choice of attention mechanism within the UAFM module affected the overall model performance. Table 6 presents a comparison of the evaluation metrics obtained after the ablation of different components, while Figure 5 shows the resulting area segmentation maps. As is evident from the table, incorporating the auxiliary encoder into U-Net yields improvements of 0.35% in the F1-score and 0.63% in the IoU, underscoring the auxiliary encoder's clear contribution to the model's overall performance. After adding the UAFM, the use of channel attention resulted in a slight performance decrease; however, employing spatial attention led to an additional gain, improving the F1 and IoU by 0.20% and 0.12%, respectively, over the result achieved with only the auxiliary encoder. A possible explanation for this outcome is that the spatial resolution of the UAV images--captured for the area dataset--is relatively high compared with their spectral resolution. Consequently, spatial attention better captures the relationships among individual trees in the scene. For this reason, we opt for spatial rather than channel attention within the UAFM. From the three sets of comparative segmentation results in Figure 7, it is evident that integrating the auxiliary encoder and UAFM significantly enhances the model's ability to learn and accurately delineate the fine-grained structures of area branches and leaves, resulting in more complete and clearer segmented boundaries.