# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
## 3 Results and Discussion
### Comparison Experiments
#### 3.3.1 Comparision Experiments for DE Module  
In this subsection, the super-resolution results under different factors are discussed. The performance of the proposed DE module was evaluated over the two selected datasets. For each dataset, a \(512\times 512\) sub-region was randomly selected from the image, in order to train the network, and another \(512\times 512\) sub-region, divided equally from the image, was used to validate the performance of our proposed method. In order to simulate a low resolution image, we first downsampled by the factor. The corresponding image size was changed to the original \(1/\text{N}\), and used as the input to the super-resolution module. For all the compared models in this experiment, we used the PSNR as the metric. Comparison experiment of the proposed DE module with different settings: the factors we set were 2, 3, and 4. The results are summarized in Tables 1 and 2. It was observed that: (1) Our proposed DE module obviously outperformed all of the other methods over these two datasets, showing the highest PSNR; (2) the PSNR value improved when we used the Deep Residual Block (DRblock); (3) when we further used the Feature Fusion Connection (FFC), the PSNR further improved. Based on the use of these two blocks, the module can extract features more accurately, and can fuse global and local residual features efficiently. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**U** & **ESPCN** & **DRblock** & **DE(DRblock + FFC)** \\ \hline
4 & 26.0611 dB & 26.2111 dB & 26.8953 dB \\
3 & 27.9854 dB & 28.8974 dB & 29.6001 dB \\
2 & 31.4781 dB & 32.3776 dB & 33.3720 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison experiment of the proposed DE module, when different settings were adopted, using the Inria aerial dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**U** & **ESPCN** & **DRblock** & **DE(DRblock + FFC)** \\ \hline
4 & 26.3522 dB & 27.0531 dB & 27.5876 dB \\
3 & 27.6836 dB & 28.8912 dB & 29.7317 dB \\
2 & 31.9551 dB & 32.7683 dB & 33.9959 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison experiment of the proposed DE module, when different settings were adopted, using the Satellite dataset II (East Asia). Comparison Experiment of the proposed DE module with different methods: in this experiment, we compared our DE module with four other state-of-the-art methods (ESPCN, EDSR, SRCNN, and DBPN). Among these methods, SRCNN and ESPCN have the simplest structure, consisting of only a few convolutional layers in the feature extraction part, where a sub-pixel upsampling layer is used in the up-sampling part of ESPCN. EDSR uses the Residual Block module, but uses an interpolation method in the up-sampling part. The structure of DBPN is the most complicated. The DE module we designed uses the same sub-pixel upsampling layer as ESPCN, and uses the Residual Block in EDSR as the feature extraction module. The factors we set were 2, 3, and 4. The results are summarized in Tables 3 and 4. It was observed that: (1) The PSNR of DE was very close to the value of DBPN and much higher than those of EDSR and SRCNN on the two datasets; and (2) when the upsampling factor increased, the superiority of our proposed DE became less obvious. This was due to the problem of SR being more difficult with a higher upsampling factor. The visual results of our proposed DE module were compared with those of five other state-of-the-art methods (bicubic, ESPCN, EDSR, SRCNN, and DBPN) on the two datasets, as shown in Figures 4 and 5. In order to facilitate comparison, we used the bicubic method (which is commonly used for up-sampling) to up-sample the low-resolution images obtained through downsampling to the size of the original image, in order to compare the visual results. It can be observed that the details in the image reconstructed using the DE module were richer. In Figure 4, compared with the SRCNN and ESPCN algorithms, objects with different sizes in the image reconstructed by DE module can be easily distinguished from the surrounding environment.