# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results
### Analysis of Implementation Results  
Compared with some classic networks, such as FCN-8s, SegNet, UNet, PSPNet, and FPN_MSFF, the FRA-Net has the biggest difference in the way of image feature extraction during the downsampling process. The evaluation methods are all deep learning-based \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Initial learning rate & 0.0003 \\ Batch size & 10 \\ Training steps & 100,000 \\ Loss function & Cross entropy loss + L2 loss \\ Optimization method & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model hyperparameter settings. Figure 4: MFA module. methods; they have different structures. These methods are designed based on a convolutional neural network, which is the relationships between them. Among them, FCN-8s, SegNet, and UNet use VGG16 as their downsampling reference network, and PSPNet and FPN_MSFF use ResNet-101 as their downsampling reference network. Table 2 shows the comparison of the model structure of the semantic segmentation network and the FRA-Net proposed in this paper and the comparison of the average prediction time of a single image in the (Aerial Imagery for Roof Segmentation) LandCover test set [36]. The LandCover dataset is a high-resolution aerial image set covering a total area of 216.27 square kilometers in Poland. The data set contains 33 pictures with a resolution of about 9000 \(\times\) 9500 and 8 pictures with a resolution of about 4200 \(\times\) 4700, and the data are RGB images. Each of the pictures is artificially labeled with four types of objects: buildings (red), woodlands (green), water (grey), and backgrounds (black). For the processing of this dataset, all images were cut from left to right and top to bottom without 264 overlapping the 512 \(\times\) 512 images. The images with only one class are eliminated, and then 265 randomly are divided into the training set and test set according to the ratio of 0.8:0.2. All experiments in this paper are based on Tensorflow deep learning framework. The software environment includes: the operating system is windows 10, Tensorflow version is 1.9.0, CUDA version in GPU acceleration library is 9.0, and cudnn version is 7.1. The hardware configuration includes: the CPU is AMD Ryzen 7 2700, the RTX 2070 8 g GPU is used for training and reasoning, and the memory is 16 g with a 512 g solid-state disk. It can be clearly seen from the table that the three indicators of FRA-Net are all better than the five contrasting semantic segmentation networks. This shows that FRA-Net has good integrity and correctness for the buildings in the LandCover test set, and the mIoU is 1.57% higher than FPN_MSFF, which has the highest index of the comparison model. In Table 2, FCN-8s has the largest number of parameters and the longest average prediction time, reaching 86.57 s/frame. However, the accuracy of the test is still high. This is mainly due to the use of the jump structure, which combines the features of the low-level and high-level, and enhances the generalization performance of the model. FPN_MSFF's mIoU index is the highest (0.8687 vs. our 0.8678) among the five comparison models but its reasoning speed is far lower than the FRA-Net proposed in this paper. Compared with FPN_MSFF, the reasoning speed of our FRA-Net is nearly twice as fast, reaching 34.57 s/frame. From a comprehensive consideration, FRA-Net has both speed and accuracy performance advantages. This is mainly due to two points. One is the improved SRM. The introduction of the residual structure makes the model easier to train, which not only alleviates the phenomenon of gradient disappearance but also guarantees good information. Depth separable convolution will not bring huge parameters while obtaining a larger receptive field, which reduces the computational complexity of the model. The second is that its symmetrical up-sampling structure makes it easier for the model to recover features.