## 4 Discussion  
### Analysis of Method Performance Superiority  
This study demonstrates the significant advantages of the UGSAM model in image defogging and semantic segmentation tasks through multidimensional metrics. The WRPM module within UGSAM employs a dual-channel branch structure to process dense fog and light fog regions in images in parallel, and subsequently integrates the extracted features via a fusion module. This approach maximizes defogging effectiveness while preserving as many original image characteristics as possible. The WRPM is capable of addressing images with reduced clarity caused by adverse weather conditions such as rain and fog, thereby enhancing the accuracy of vegetation localization. Additionally, the SCP-Net component of the UGSAM refines the segmentation results produced by the SAM, further improving localization precision. In the comparative experiments, on one hand, we conducted a performance comparison between the defogging WRPM and other defogging networks. The results are presented in Table 4. In the experimental results, the LPIPS metric indicates that the defogged images by the WRPM are perceptually closer to ground truth and possess richer details. The results of SSIM and PSNR suggest that the defogged images generated by the WRPM have the minimal difference from the ground truth. In Figures 5-7, the results processed by different defogging methods are visualized. Through comparison, it can be found that the WRPM is always the closest to the ground truth. On the other hand, we tested the vegetation segmentation performance of the UGSAM method under normal and adverse weather conditions. The segmentation accuracy directly determines the positioning accuracy. The results under normal weather conditions are presented in Table 5, and those under adverse weather conditions are shown in Table 6. In adverse weather conditions, the UGSAM achieved extremely significant improvements over the SAM in multiple indicators, with OA increasing by 16.29%, and realized high-precision positioning of vegetation areas. Under normal weather conditions, the OA of the UGSAM was only 1.03% higher than that of the SAM, which is not significant. However, the main contribution of this study lies in the scenarios of severe weather. Therefore, the improvement brought about by normal weather is an additional contribution. In the future, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **FL** & **CEL** & **DL** & **OA** & **Re** & **Pr** & **mIoU** \\ \hline \multirow{4}{*}{FCN} & ✓ & ✗ & ✗ & 0.6889 & 0.7984 & 0.7779 & 0.6993 \\  & ✗ & ✓ & ✗ & 0.6913 & 0.7996 & 0.7772 & 0.6998 \\  & ✗ & ✗ & ✓ & 0.6908 & 0.8001 & 0.7831 & 0.7006 \\  & ✗ & ✓ & ✓ & **0.6935** & **0.8038** & 0.7878 & **0.7028** \\  & ✓ & ✓ & ✓ & 0.6921 & 0.7996 & **0.7895** & 0.7003 \\ \hline \multirow{4}{*}{PointNet++} & ✓ & ✗ & ✗ & 0.7516 & 0.7592 & 0.8192 & 0.6863 \\  & ✗ & ✓ & ✗ & 0.7524 & 0.7604 & 0.8210 & 0.6886 \\  & ✗ & ✗ & ✓ & 0.7552 & 0.7597 & 0.8199 & 0.6907 \\  & ✗ & ✓ & ✓ & **0.7562** & **0.7665** & **0.8242** & **0.6989** \\  & ✓ & ✓ & ✓ & 0.7531 & 0.7614 & 0.8231 & 0.6897 \\ \hline \multirow{4}{*}{UGSAM} & ✓ & ✗ & ✗ & 0.8542 & 0.9173 & 0.9001 & 0.8403 \\  & ✗ & ✓ & ✗ & 0.8599 & 0.9246 & 0.9027 & 0.8417 \\ \cline{1-1}  & ✗ & ✓ & ✓ & 0.8603 & 0.9196 & 0.9035 & 0.8410 \\ \cline{1-1}  & ✗ & ✓ & ✓ & **0.8632** & **0.9277** & 0.9048 & **0.8452** \\ \cline{1-1}  & ✓ & ✓ & ✓ & 0.8620 & 0.9264 & **0.9062** & 0.8437 \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold.  
\end{table} 
Table 9: Effects of different loss functions on SCP-Net performance. Focal loss (i.e., FL), cross-entropy loss (i.e., CEL), Dice loss (i.e., DL), recall (i.e., Re), precision (i.e., Pr). we hope to achieve a breakthrough in the positioning accuracy of vegetation under normal weather conditions. In the ablation experiments, we conducted three distinct tests.