# 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  
In order to further verify the effectiveness and generalization performance of the model proposed in this paper, the Massachusetts building dataset will be tested. We selected several representative areas, namely residential areas, rural areas, and complex areas, and received the prediction effect map as shown in Figure 8. In Figure 8, green represents true-positives (predicted as buildings, actual buildings); blue represents a false-positive (predicted as a building, but actually a non-building); white represents true-negatives (predicted to be non-building, but actually non-building); red represents false-negatives (predicted as non-buildings, actual buildings), M represents the mIoU, F represents the F1 score, and P represents the accuracy rate. Figure 7: Comparison of two kinds of datasets. (**a**) residential area; (**b**) rural area; (**c**) seaside area. Figure 8: _Cont._  
satisfy so many dense buildings, and it is difficult for the model to extract the different features. In rural areas, the segmentation difference of each model is more obvious. Both the SR-SegNet and FRA-Net proposed in this paper predict good results, but there are also some areas that are easy to miss, mainly because the features of missed buildings are quite different from other buildings. The number is small, which easily leads to insufficient model learning. For complex regions, the prediction effects of several models are not very good, and there are a lot of missed detections and false detections. Among them, FPN_MSFF has the best segmentation effect, followed by SR-SegNet and FRA-Net. Table 4 shows the evaluation results of different models in the Massachusetts building test set. There are 10 remote sensing images with 1500 \(\times\) 1500 in the Massachusetts building test set. It can be found from the table that the SR-SegNet model has the highest precision, 1.64% higher than the classic SegNet, and 4.96%, 3.71%, 1.99%, and 1.35% higher than FCN-8s, UNet, PSPNet, and FPN_MSFF, respectively. The F1 score also reached 0.8149, and the F1 score of FRA-Net proposed in this paper reached 0.8164, the highest among several models, indicating that the false detection rate is low. In the mIoU evaluation index, the five comparison models have very little difference, but SR-SegNet and FRA-Net reached highs of 0.8062 and 0.8049, respectively, indicating that the model's segmentation is more accurate. It is worth noting that FRA-Net's inference for a single remote sensing image is only 1.23 s in terms of prediction time, which is 3.17 times faster than FCN-8s. This will be verified again on the Massachusetts building dataset, indicating that the FRA-Net model has good generalization performance, and other comparison models SegNet, UNet, PSPNet, and FPN_MSFF are not as fast as FRA-Net predictions. Compared with the LandCover dataset, the mIoU of all models is nearly 10% lower than the above index. The main reason is that the pixel resolution of the Massachusetts building dataset is only 1500 \(\times\) 1500, and the buildings are very dense. Therefore, the visibility of the cropped 256 \(\times\) 256 images is reduced, which causes the model to encounter resistance in feature extraction. In addition, the sample size of the dataset is also a large reason. The LandCover dataset has 857 original images with a resolution of 10,000 \(\times\) 10,000 that have been cropped for training, while the Massachusetts building dataset has only 137 original images at 1500 \(\times\) 1500. At the same time, the building scenes are not evenly distributed, so the training results are not as good as the LandCover dataset. Overall, the two models, SR-SegNet and FRA-Net, proposed in this paper have indeed achieved good results. From the evaluation indicators tested on the Massachusetts building dataset in Table 4, it can be seen that among the five comparative models, PSPNet's overall indicators are the best.