## 4 Results
### Comparison Experiment of the WHDLD  
In order to further verify the robustness and generalization of GSRPnet and the correctness of our proposed hypothesis, we conducted experiments on the WHDLD. In this section, we follow the methods of [26], who randomly selected 80% of the WHDLD as the training set and 20% as the testing set. We set the learning rate, number of epochs, and batch size to 0.001, 120, and 10, respectively.  
As shown in Table 5, the advantage of GSRPnet is further expanded. Compared with SGCNnet, which achieved the second-best result, GSRPnet achieved 0.116%, 1.458%, 1.989%, and 0.791% improvements in Pre, the F1-score, IoU, and OA, respectively, and the number of model parameters is less than half of that of SGCNnet. Compared with the other four models, GSRPnet exhibits varying degrees of improvement in all indicators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Pre \(\uparrow\)** & **F1-Score \(\uparrow\)** & **IoU \(\uparrow\)** & **OA \(\uparrow\)** & **Params \(\downarrow\)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(M)** \\ \hline U-net & 88.449 & 88.770 & 80.273 & 91.749 & **14.789** \\ PSPnet & 86.884 & 87.965 & 79.096 & 91.166 & 52.495 \\ Segnet & 84.259 & 85.220 & 74.768 & 89.068 & 29.444 \\ DeepLab V3+ & 85.977 & 86.250 & 76.380 & 89.995 & 59.339 \\ SGCNnet & 88.022 & 88.996 & 80.619 & 91.856 & 43.908 \\ GSRPnet & **89.328** & **89.427** & **81.344** & **92.256** & 17.999 \\ \hline \hline \end{tabular} The \(\uparrow\) and \(\downarrow\) indicate the larger the better and the smaller the better, respectively.  
\end{table}
Table 4: Quantitative comparison of the results on the GS2018 test dataset. The best and second-best results are highlighted in bold and underlined, respectively.  
Figure 12 shows the visualized results of UGS segmentation for all models on the WHDLD testing set. Similar to the results of GS2018, when faced with scattered UGS, U-net still struggles to discern effectively, and there is a phenomenon of misclassification. PSPnet is insensitive to continuous strip-like UGS. Segnet is the least effective model, and it cannot distinguish well when the spectral information of adjacent objects is similar. DeepLab V3+ shows a serious misclassification when dividing scattered UGS, particularly in residential areas. SGCNnet does not produce fine UGS well in the area of interleaving UGS and non-UGS. The GSRPnet proposed in this paper still shows the best results on the WHDLD, which is the closest to the ground truth, and further proves the advanced ability of GSRPnet in the UGS segmentation task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Pre \(\uparrow\)** & **F1-Score \(\uparrow\)** & **IoU \(\uparrow\)** & **OA \(\uparrow\)** & **Params \(\downarrow\)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(M)** \\ \hline U-net & 85.845 & 84.491 & 74.801 & 89.213 & **14.789** \\ PSPnet & 84.087 & 83.438 & 73.391 & 88.412 & 52.495 \\ Segnet & 85.258 & 81.948 & 71.445 & 87.802 & 29.444 \\ DeepLab V3+ & 83.348 & 83.835 & 72.736 & 87.152 & 58.036 \\ SGCNnet & 87.664 & 84.567 & 75.003 & 89.582 & 43.908 \\ GSRPnet & **87.830** & **86.025** & **76.992** & **90.373** & 17.992 \\ \hline \hline \end{tabular}  
* The \(\uparrow\) and \(\downarrow\) indicate the larger the better and the smaller the better, respectively.  
\end{table}
Table 5: Quantitative comparison results on the WHDLD test dataset. The best and second-best results are highlighted in bold and underlined, respectively.  
Figure 11: UGS segment results using the GS2018 test dataset. **(a)** Satellite imagery, **(b)** ground truth, (c)** GSRPnet, **(d)** Unet, **(e)** PSPnet, **(f)** Segnet, **(g)** DeepLab V3+, and **(h)** SGCNnet.