# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 3 Results
### Ablation Experiments  
#### 3.2.1 Ablation Experiment on UGS-1 and UGS-2  
To verify the effectiveness of the ResNet50, MSPA, BFF, and DE modules, ablation experiments are performed on UGS-1 dataset (Table 3) and UGS-2 dataset (Table 4), respectively. We start with the first line for feature extraction using the baseline, and then the upsampling operation is directly performed to output the extraction results. From the second line to the last line, this article adds DE, BFF, Pyconv, GAM, and MSPA modules in sequence. Figure 10: Visualization results of experiments comparing the UGS-2. (**a**) Input image; (**b**) Label; (**c**) DensASPP; (**d**) DeepLabv3+; (**e**) UNet; (**f**) MAFANet. Compared with the base network, the model feature extraction performance can be improved by using the new decoder module (Table 3, Figure 11). The DE module protects the integrity of the information when extracting UGS features, resulting in a 0.33% improvement in MIOU, a 0.41% improvement in IOU (low vegetation), and a 0.12% improvement in IOU (tree). The BFF module enhances the dual-channel communication capability, so that the two neighboring layers of ResNet50 network can guide each other in feature mining and improve the anti-interference capability of the MAFANet model, resulting in a 0.54% improvement in MIOU, a 0.37% improvement in IOU (low vegetation), and a 0.84% improvement in IOU (tree). Using the MSPA module, MIOU is significantly improved by 0.43%. In addition, a comparison was made between Pyconv and the GAM module, both of them showed a decrease in performance. The optimized MSPA module has better performance, with MIOU, IOU (low vegetation) and IOU (tree) all improved. The reason is that the MSPA module takes into account the multi-scale information extraction of the Pyconv module and the GAM module. The MSPA effectively utilizes the global attention field, which in turn improves the segmentation accuracy of the MAFANet model. Using the above improvement strategy finally brings a gain of 1.04 percentage points compared with the baseline, thus proving the effectiveness of the MAFANet network. Compared with the base network, the model feature extraction performance can be improved by using the new decoder module (Table 4, Figure 12). Building the DE module and the BFF module effectively guides the learning of adjacent two layers of ResNet50 network and can carry out feature mining at the same time, increasing MIOU by 0.39%, IOU (low vegetation) by 0.52%, and IOU (tree) by 0.56%. Using the MSPA module, MIOU is significantly improved by 0.43%. In addition, comparing the Pyconv and GAM modules, the optimized MSPA module has better performance, with the MIOU, IOU (low vegetation) and IOU (tree) all improved. The reason is that the MSPA module takes into account the multi-scale information extraction of the Pyconv module and the GAM module. As a result, the MSPA module effectively improves segmentation accuracy. Using the above improvement strategy finally brings a gain of 0.82 percentage points to the model, proving the effectiveness of the MAFANet network. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{4}{c}{**Class IOU**} \\ \hline
**Method** & **MIOU (\%)** & **Low Vegetation (\%)** & **Tree (\%)** \\ \hline baseline & 73.82 & 50.71 & 82.68 \\ baseline+ DE & 74.15 & 50.97 & 83.00 \\ baseline+ DE+ BFF & 74.21 & 51.23 & 83.24 \\ baseline+ DE+ BFF+ Pyconv & 74.46 & 51.24 & 83.43 \\ baseline+ DE+ BFF+ GAM & 74.48 & 51.11 & 83.71 \\ baseline+ DE+ BFF+ MSPA & 74.64 & 51.43 & 83.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of UGS-2 ablation experiments. DE denotes decoder block, BFF denotes bilateral feature fusion module, Pyconv denotes Pyramidal Convolution, GAM denotes Global attention mechanism, MSPA denotes multiscale pooling attention.