## 5 Discussion  
Benefiting from the high-spatial resolution and relatively short revisit period of remote sensing imagery, extracting UGS information from such imagery proves to be a quick and effective approach. Furthermore, with the development of deep learning technology, semantic deep segmentation neural networks can achieve higher accuracy compared with traditional methods. However, the exiting deep segmentation neural networks still encounter challenges when applied directly to the UGS segmentation task. After studying the characteristics of UGS, we found that a UGS is a kind of low-rank features. Because of the characteristics of low-level features, the detection of a UGS is not highly dependent on the model's ability to understand semantic features, which means that the recognition ability of UGS surface texture and color features is more important. In order to further research on the UGS segmentation task with deep learning technology, we proposed an end-to-end UGS segmentation model named GSRPnet. In comparison with five other segmentation models [27; 28; 29; 30; 31], GSRPnet exhibits several advantages as follows:  
1. In addressing the challenges posed by small-scale and fragmented UGS, GSRPnet outperforms the other five models. For instance, in scenarios where roads and buildings are surrounded by trees or other types of UGS (Figure 11, sixth row and Figure 12, second row), GSRPnet excels in clearly distinguishing their outlines without interruptions or omissions. In contrast, Segnet [29] and DeepLab V3+ [30] struggle to discern surface objects distinctly and may even misclassify non-UGSs as UGS. This discrepancy arises from the fact that these models use regular down-sampling methods (such as convolution, resampling, etc.) at the model input stage to conserve  
Figure 12: UGS segment results under the WHDDL test dataset. (**a**) Satellite imagery, (**b**) ground truth, (**c**) GSRPnet, (**d**) Unet, (**e**) PSPnet, (**f**) Segnet, (**g**) DeepLab V3+, and (**h**) SGCNnet. computational resources. However, these regular down-sampling methods result in the loss of texture information of the input images. GSRPnet utilizes the reverse Pixel Shuffle method to reduce image size and minimize the loss of UGS information. 2. In GSRPnet, multi-scale feature maps (as shown in Figure 7, F2-F4) are concatenated to offer multi-level UGS feature information for the MLP layer. This strategy of concatenating feature maps ensures the provision of multiple and different levels of UGS information for subsequent MLP layer decisions. The key advantage of this strategy is that GSRPnet becomes more responsive to both non-UGS and UGS boundaries. This strategy's effectiveness is also evident in other comparative models using similar approaches, such as the Unet [27] model with a skip-connection strategy, which also exhibits clear boundaries. However, the concatenation strategy in GSRPnet is more effective. 3. From the experiments on both the GS2018 dataset and WHDLD, as shown in Tables 4 and 5, GSRPnet achieved the best results in all the four precision measurement metrics. Specifically, GSRPnet achieved Pre, F1-score, IoU, and OA values of 89.328%, 89.427%, 81.344%, and 92.256% and 87.830%, 86.025%, 76.992%, and 90.373% for the GS2018 dataset and WHDLD, respectively. In addition, the number of model parameters (Params) is also a key indicator for measuring the effectiveness of a model. Generally, increasing the depth of the network or increasing the number of model parameters using other means can improve the recognition accuracy of a model. However, this improvement comes at the cost of increased computational resources. In comparison with the other four models, GSPRnet utilizes the second fewest Params (only slightly higher than Unet by 3.21 million), yet it achieves the best results. This clearly demonstrates that GSRPnet aims not only to enhance accuracy by indiscriminately increasing the number of network parameters.