# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 4 Discussion  
Among the existing studies on UGS extraction based on HRS images, our study further realizes the classification of tree and low vegetation compared to the study by [PERSON] et al. [40]. Compared to [PERSON] et al. [41], we segment tree with higher accuracy when considering multi-scale object feature information in UGS. Compared with [PERSON] et al. [42] and [PERSON] et al. [43], we further integrate feature engineering to improve the  
Figure 13: Feature engineering ablation experiment visualization results. (**a**) Input image (UGS-1); (**b**) Label; (**c**) MAFANet (NDVI); (**d**) MAFANet (UGS-1); (**e**) MAFANet (UGS-1+NDVI); (**f**) MAFANet (UGS-2); (**g**) MAFANet (UGS-2+NDVI). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Class IOU**} \\ \hline
**Data** & **Method** & **MIOU (\%)** & **Low Vegetation (\%)** & **Tree (\%)** \\ \hline \multirow{2}{*}{NDVI} & UNet & 69.58 & 41.84 & 79.83 \\  & MAFANet & 71.10 & 46.26 & 80.38 \\ \hline \multirow{2}{*}{UGS-1} & UNet & 71.53 & 48.94 & 81.12 \\  & MAFANet & 72.15 & 49.53 & 81.64 \\ \hline \multirow{2}{*}{UGS-1+NDVI} & UNet & 73.48 & 49.61 & 82.71 \\  & MAFANet & 74.09 & 50.67 & 83.34 \\ \hline \multirow{2}{*}{UGS-2} & UNet & 74.09 & 50.71 & 83.28 \\  & MAFANet & 74.64 & 51.43 & 83.72 \\ \hline \multirow{2}{*}{UGS-2+NDVI} & UNet & 74.11 & 50.89 & 83.26 \\  & MAFANet & 74.73 & 50.96 & 84.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the feature engineering ablation experiments. UGS-1+NDVI indicates that NDVI is superimposed onto the UGS-1 data, and UGS-2+NDVI indicates that NDVI is superimposed onto the UGS-2 data. UGS segmentation accuracy. In view of the problem that the current DL methods for UGS segmentation are too focused on improving the model structure but ignore the spectral information of HRS images, we proposed and verified a novel MAFANet. The MAFANet achieved UGS segmentation from high-resolution GF-2 satellite images by incorporating feature engineering and DL technology. We built UGS labeled datasets UGS-1 and UGS-2 based on GF-2 images to test the applicability of DL in UGS segmentation. At the same time, we conducted ablation experiments on the datasets UGS-1 and UGS-2 to verify the effectiveness of each module of the MAFANet network. In addition, deep learning networks such as FCN8s, UNet, SegNet, DeepLabv3+, DenseASPP, ShuffleNetV2, PSPNet, DFANet, DABNet, ESPNetv2, ACFNet, ERFNet, HRNet, DensASPP (mobilenet), MFFTNet were selected for comparative experiments. Experimental results on UGS-1 and UGS-2 data set show that our MAFANet model exhibits good robustness. In addition, we found that the MFFTNet network performed erratically. In the UGS-1 dataset, MFFTNet had a higher IOU for segmenting two vegetation than PSPNet. In the UGS-2 dataset, MFFTNet had a higher IOU for segmenting low vegetation and a lower IOU for segmenting tree than UNet. Both UNet and MAFANet performed well, with a good MIOU in both the UGS-1 and UGS-2 datasets, segmenting the tree and low vegetation very steadily. To this extent, we performed ablation experiments for feature engineering in the UNet and MAFANet networks. In particular, by using false color synthesis and vegetation feature (NDVI) feature engineering, our MAFANet network improved the MIOU by 2.58% over the baseline network, thus, further demonstrating the effectiveness of our approach. In particular, the use of false color compositing was better for UGS segmentation, with a significant increase of 2.49% for MAFANet. In contrast, NDVI was less effective, and incorporating NDVI into MAFANet resulted in a MIOU increase of 1.94%. Overall, results showed that the MIOU of UGS segmentation can be significantly improved by incorporating false color synthesis and the vegetation index (NDVI) into our MAFANet model. However, although our MAFANet model showed excellent performance in experiments, we also recognize that the training of DL models still requires a large amount of labeled data, which may limit its generalizability in practical applications.