# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results
### Analysis of Implementation Results
#### 3.2.3 Analysis of Results
Therefore, this model and the two models proposed in this paper are selected for qualitative prediction and analysis, mainly from the feature extraction of small-size buildings and large-size buildings. In order to observe the detailed information of the test picture segmentation more intuitively, the three pictures in the test set are selected and cropped to a 512 \(\times\) 512 size, and the local area map obtained by the model weight prediction is shown in Figure 8.  
In Figure 9, green represents true-positives (predicted as buildings, actual buildings); blue represents false-positives (predicted as buildings, actual non-buildings); white rep \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall Rate**} & \multirow{2}{*}{**F1 Score**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Prediction Time**} \\  & & & & & **Inter-Section Ratio** & **(Seconds/Frame)** \\ \hline FCN-8s & 0.8095 & 0.7450 & 0.7757 & 0.7717 & 3.91 \\ SegNet & 0.8427 & 0.7192 & 0.7755 & 0.7727 & 2.56 \\ UNet & 0.8220 & 0.7158 & 0.7650 & 0.7630 & 2.23 \\ PSPNet & 0.8392 & 0.7587 & 0.7965 & 0.7899 & 2.72 \\ FPN_MSFF & 0.8456 & 0.7294 & 0.7827 & 0.7788 & 2.59 \\ SR-SegNet & **0.8591** & **0.7755** & 0.8149 & **0.8062** & 1.95 \\ FRA-Net & 0.8582 & 0.7739 & **0.8164** & 0.8049 & **1.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of different methods in the Massachusetts building test set. resents true-negatives (predicted as non-buildings, actual non-building); red represents a false-negative (predicted as a non-building, but actually a building). In Figure 9a, the prediction of small-sized buildings in the Massachusetts building dataset is made. It can be found that the overall segmentation effect of the three models for small-sized buildings is still better, and most buildings can be accurately predicted. It can be observed in the black circled area in Figure 9a that for the yellow buildings in the original image, PSPNet missed a part of the detection, but the surrounding black and white buildings have been identified. In contrast, SR-SegNet and FRA-Net can perform good recognition, which also reflects that the generalization of these two models is better than PSPNet. Figure 9b shows the prediction effect of large buildings. There are more red and blue areas in the figure, indicating that the three models have more cases of missed and false detections. However, it is not difficult to find some details from the black circled area in Figure 9b. PSPNet missed the small size building next to the large building, while SR-SegNet and FRA-Net recognized this small size well. This shows, to a certain extent, that these two models can recognize buildings in more complex scenes. This may benefit from the symmetrical structure of the two models, which facilitates better recovery of the extracted features. In addition, the effect of PSPNet on building boundary restoration in Figure 9b is slightly better than that of SR-SegNet and FRA-Net. The scene in Figure 9c is more complicated, and the black circled area in the figure is part of the area that the three models missed. Because there are many distractors in this scene, such as cars and containers, and there are many irregularly shaped buildings, this makes the model judgment wrong, which is also one of the problems to be solved in the future. Figure 9: _Cont._