## 5 Results  
### Comparison with Other Methods  
In order to assess the performance of the proposed UniU-Net, we compared it against several existing methods, including the classic U-Net [32] and ResUnet, as well as five improved models: MAResUNet [37], ABCNet [38], BANet [39], CREVNet [40], and LiViTNet [41]. To ensure consistent and comparable results, all of these networks were trained using the same training strategy and were not subjected to any form of pre-training.  
Table 3 summarizes the extraction accuracy achieved by various methods, while Figure 4 contrasts the segmentation maps generated by different models. Overall, UniU-Net yields the highest segmentation accuracy in terms of the F1-score and IoU, although its accuracy (Acc) is marginally lower than that of U-Net. More specifically, UniU-Net attains an F1-score of 79.20%, surpassing the second-best model, U-Net, by 1.30%. In terms of the IoU, UniU-Net achieves 65.56%, outperforming the next-best model by 1.77%.  
From the segmentation predictions illustrated in Figure 5, UniU-Net provides the most coherent results, exhibiting sharper boundaries and more realistic details. In the first and second rows, other methods produce coarser segmentation around tree leaves and branches, whereas UniU-Net aligns more closely with the ground-truth labels. In the third and fourth rows, competing methods display noticeable under-segmentation, resulting in discontinuous and blurry edges. By contrast, UniU-Net maintains a high level of stability, delineating the area palms with continuous and distinct boundaries. On the other hand, constrained by the low data resolution and limited spectral bands, all models exhibit instances of individual trees being indistinguishable in areas where they are densely distributed. Comparatively, UniU-Net demonstrates the smallest amount of adhesion and offers better differentiation.  
The parameters and inference speed of the model are also crucial metrics in practical applications. We compared the parameter counts and inference speeds of the different methods, as detailed in Table 4. The results indicate that the UniU-Net proposed in this study, despite having the largest number of parameters, achieves the second-best inference speed at 53 FPS. Benefiting from structural reparameterization theory, maintaining the same structure for the auxiliary encoder as the primary encoder not only reduces the risk of model overfitting but also optimizes the inference speed during prediction, allowing UniU-Net to stand out.  
Overall, UniU-Net precisely identifies and delineates the true edges of the area palms, offering a clear advantage in terms of segmentation accuracy compared with existing approaches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Acc (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline U-Net & **92.33** & 77.90 & 63.79 \\ ResUnet & 91.72 & 76.18 & 61.52 \\ MAResUNet & 92.20 & 77.57 & 63.35 \\ ABCNet & 91.46 & 75.62 & 60.79 \\ BANet & 91.07 & 73.75 & 58.42 \\ CREVNet & 91.51 & 76.34 & 61.73 \\ LiViTNet & 91.59 & 77.65 & 63.47 \\ UniU-Net (ours) & 92.30 & **79.20** & **65.56** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of segmentation results of different methods.  
Figure 5: Examples of semantic segmentation results of different methods. (**a**) U-Net. (**b**) ResU-Net. (**c**) MAResU-Net. (**d**) ABCNet. (**e**) BANet. (**f**) CREVNet. (**g**) LiViTNet. (**h**) UniU-Net (ours).