# 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
Figure 5: Local prediction of 1–4 sampling methods in the LandCover dataset, the meaning of colors is buildings (red), woodlands (green), water (grey), and backgrounds (black). (**a–e**) are the experimental results of 1-4 methods on images from the LandCover dataset. Figure 5 shows several instance results for different categories. It can be seen from the observations that the resulting map predicted by FRA-Net is the closest to the original image, whether it is for woodland, water, or buildings. The results in the third column of Figure 5 are the worst predictions for buildings throughout the experiment, with many buildings being misclassified as non-buildings. At the same time, woodland areas are also misclassified as waters in Figure 5b-d. It is worth noting that methods 2 to 6 have fewer misjudgments in this regard. Except for method 1, the rest of the model structures use the residual structure for downsampling, and the same ordinary residual block is used for the upsampling process from method 1 to 3. This also explains the effectiveness of the residual structure as an upsampling process to some extent. From the five scenarios in the figure, method 1 has the highest false-positive rate of buildings. Residential houses were misjudged due to different roof shapes when dividing houses, and flat-roofed houses were identified as non-buildings. When dividing buildings for shopping malls and factories, they will be identified as non-buildings due to the architectural pattern of the roof. In contrast, methods 2 to 4 use residual blocks to extract features during downsampling, and the false identification rate of buildings is lower. This is because the \"skip structure\" of the residual block is stronger than the structural gradient propagation of VGG16, and can also perform effective back-propagation, which enhances the generalization performance of the model. Therefore, it can extract effective features better than VGG16, and the feature recognition of buildings is also more accurate. The only difference between method 2 and method 3 is the method of downsampling. One is the extraction method using ResNet-101, and the other is the extraction method consisting of five residual blocks. It can be seen from Table 3 that the downsampling method composed of residual blocks is about 1% higher than ResNet-101 in F1 score and mean Intersection over Union (mIoU). From the regions predicted to be buildings in the fourth and fifth columns of Figure 5, it can be seen that the extraction method consisting of five residual blocks is slightly better than the ReNet-101 extraction method in these parts. More importantly, the average time of predicting the entire set is reduced by about 20 s compared with method 2. Therefore, for remote sensing images, the deeper the convolutional layer may not necessarily achieve better prediction results. In the model prediction using method 4, there is no big difference between the segmentation accuracy index of method 3 in Table 3, but the average prediction time of a single image is shortened by about 15 s. In addition, from the prediction effect of the sixth column in Figure 5, it can be seen that the processing of complex and non-complex regions is not much different from that of method 3, but the segmentation effect is obviously better than that of method 2. This also illustrates the effectiveness of the SRM constructed in this paper. On the one hand, by increasing the width of the convolutional neural network to obtain more characterization information. On the other hand, a large convolution kernel of 3 \(\times\) 3 or 5 \(\times\) 5 is used to obtain a larger receptive field, and at the same time, the idea of depth separable convolution is introduced to reduce model parameters. Methods 5 and 6 are ablation experiments on the hollow space pyramid pooling module and the FPP module proposed after the improvement of this chapter. The mIoU of method 6 is higher than that of method 5.