# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results
### Analysis of Implementation Results
#### 3.2.1 Impact of Sampling Method on Extraction Results  
In order to reflect the effectiveness of the FRA-Net model proposed, as well as the role and advancement of each module in the upsampling and downsampling process, this paper will show the exploration process carried out on the LandCover dataset. The FRA-Net structure built in this paper is also analyzed and experimented with around the two processes of downsampling and upsampling. In the downsampling process, the common approach adopted by existing semantic segmentation algorithms is to use classic classification networks, such as VGG16 and ResNet. Among them, ResNet-50, ResNet-101, and ResNet-152 are the most commonly used backbone networks for semantic segmentation. Through the ablation experiment of each module, the test results shown in Table 3 are obtained. Among them, RB represents the ordinary residual module, and its structure is shown in Figure 2a; SRM represents the SRM built with the idea of residual structure in this paper; ASPP is the hollow space pyramid pooling module proposed in DeepLab_v2; FPP means the FPP module improved in this chapter; MFA means the MFA module improved. It should be noted that all experimental settings and experimental environments are the same. Figure 5 is a graph of prediction effects obtained by trying different downsampling and upsampling methods. In Figure 5, the red area is our main prediction object, which is buildings, the green area is predicted to be woodland, the gray area is predicted to be water, and other areas are classified as black. Table 3 shows the evaluation results of different sampling methods in the LandCover test set. From an overall point of view, compared to other methods, the FRA-Net proposed in this paper has achieved better results in these several prediction scenarios. Figure 5a-c show a residential area. The biggest feature of the residential area is that there are many trees and the distance between houses is small. From the remote sensing image, it is very dense, and the shape and size of the houses are similar. The biggest problem that often exists when dividing these residential houses is that the shelter caused by some tall trees blocks the disturbance caused by the roofs of the houses. Figure 5d,e are the areas of some public facilities and factories. Compared with houses in residential areas, these areas are quite different. First, the buildings in residential areas are mostly private houses, which are different in shape and size from the shopping malls and factory buildings in public facilities. Generally speaking, the shapes of shopping malls and factories are more diverse. From the remote sensing image, the appearance of the roof is very different from that of ordinary houses. There are usually some pipes, exhaust fans, and other public facilities on the roof, which cause great interference to the division of the building. Second, the surrounding environment of residential areas and shopping malls and factories are different. Residential areas usually consist of houses and green vegetation, so the scene is relatively simple. While most of the surrounding shopping malls and factories are cars, roads, and some containers, which appear to be relatively empty as a whole, but there are many interference objects, which also brings inconvenience to feature extraction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Downsampling** & **Upsampling** & **F1 Score** & **mIoU** &
\begin{tabular}{c} **Prediction** \\ **Time/s** \\ \end{tabular} \\ \hline Method 1 & VGG16 & RB & 0.8948 & 0.8189 & 31.26 \\ Method 2 & ResNet-101 & RB & 0.9054 & 0.8277 & 57.21 \\ Method 3 & RB & RB & 0.9119 & 0.8439 & 36.58 \\ Method 4 & SRM & SRM & 0.9157 & 0.8555 & **21.13** \\ Method 5 & SRM & SRM + ASPP & 0.9197 & 0.8586 & 24.02 \\ Method 6 & SRM & SRM + FPP & 0.9221 & 0.8604 & 23.60 \\ FRA-Net & SRM & SRM + FPP + MFA & **0.9267** & **0.8678** & 24.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of different sampling methods in the LandCover test set.