# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
## 3 Results and Discussion
### Comparison Experiments
#### 3.3.3 Comparison Experiments for Different Semantic Methods  
In this experiment, ResNet50 with ASPP was set as the basic method. The quantitative results of this experiment, using the two datasets, are listed in Tables 7 and 8. Compared with other state-of-the-art semantic methods, our method had a better segmentation effect. Compared with the basic method, our method improved the performance, in terms of the mIoU, from 0.6412 to 0.9034 (i.e., constituting an improvement of mIoU by almost 28.2%) and the PA was improved from 0.6826 to 0.8421 (i.e., improved by 24.5%) when using the Inria aerial dataset. When using the Satellite dataset II (East Asia), our method improved the performance, in terms of the mIoU, from 0.6981 to 0.8936 and the PA from 0.7408 to 0.8738, thus improving the mIoU by almost 28% and the PA by 17%. The reason for this is that the connection method of dilated convolutution and pooling in the parallel branch of the proposed pyramid structure could extract richer contextual information. At the same time, due to the use of the super-resolution algorithm in the DE module, the low- and high-frequency information in the network could be better utilized. Therefore, it was more suitable for remote sensing images. From the visualization results, we determined that our method had an ideal segmentation effect, especially for small building objects, as can be seen in Figures 10 and 11. The methods used in the experiment used pooling operations, dilated convolutions, and pyramid structures, in order to obtain contextual information. Therefore, almost all the ground objects and boundaries were correctly identified when using our method. Similar to the results in Figures 10 and 11, our method also outperformed the other methods on the Satellite dataset II (East Asia). It can be seen that our method could segment accurate contours for complex-shaped building objects. The building objects in Figures 12 and 13, especially the adjacent buildings, were accurately segmented, regardless of whether they were large- or small-sized buildings. In particular, the shape and location of small-sized buildings were consistent with the ground-truth. These findings show that our method can effectively improve the performance of remote sensing image semantic segmentation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mIoU** & **PA** \\ \hline ResNet50 + ASPP & 0.6412 & 0.6826 \\ DeepLabV3+ & 0.7967 & 0.8342 \\ ENet & 0.7127 & 0.8121 \\ ESPNet & 0.7467 & 0.8042 \\ Proposed Method(LResNet50 + WASP(AVE) + DE + GFG) & 0.9034 & 0.8421 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison quantitative results of different methods using Inria aerial dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mIoU** & **PA** \\ \hline ResNet50 + ASPP & 0.6981 & 0.7408 \\ DeepLabV3+ & 0.7416 & 0.8042 \\ ENet & 0.7758 & 0.8221 \\ ESPNet & 0.8167 & 0.8442 \\ Proposed Method(LResNet50 + WASP(AVE) + DE + GFG) & 0.8936 & 0.8738 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of quantitative results using Satellite dataset II (East Asia). Figure 11: Comparison results of semantic segmentation methods. (**a**) Input image; (**b**) ground truth; (**c**) ResNet50 + ASPP; (**d**) DeepLabV3+; (**e**) ENet; (**f**) ESPNet; (**g**) Our method (LResNet50 + WASP(AVE) + GFG). Figure 10: Comparison results results of semantic segmentation methods. (**a**) Input Image (**b**) Ground Truth; (**c**) ResNet50 + ASPP; (**d**) DeepLabV3+; (**e**) ENet; (**f**) ESPNet; (**g**) Our method (LResNet50 + WASP(AVE) + GFG). Figure 12: Comparison results of semantic segmentation methods using Satellite dataset II (East Asia). (**a**) Input image; (**b**) ground truth; (**c**) ResNet50+ASPP; (**d**) DeepLabV3+; (**e**) ENet; (**f**) ESPNet; (**g**) Our method(LResNet50+WASP(AVE)+GFG). Figure 13: Comparison results of semantic segmentation methods using Satellite dataset II (East Asia).