# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 4 Results and Analysis
### Inria Aerial Image Labeling Dataset  
In this subsection, we investigate the transferability of DHAU-Net by training all models on the Inria Aerial Image Labeling Dataset. All models are completely the same except for the different datasets. The specific quantitative evaluation indicators are shown in Table 5, and the detailed visual effect display diagram is shown in Figure 9.  
Table 5 shows the quantitative metrics results of the comparative methods and DHAU-Net on the Inria Aerial Image Labeling Dataset. DHAU-Net achieved accuracy, mIoU, precision, recall, and FI_scores of 0.9688, 0.9089, 0.9519, 0.8897, and 0.9197, respectively. In comparison to the original U-Net, all five quantitative evaluations are directly improved. Among them, U-Net + CBAM achieves the highest recall of 0.8909, but all other methods perform lower than DHAU-Net. These results verify the effectiveness of attention mechanisms in improving the segmentation quality of remote sensing images, with the DHA module demonstrating the best performance in enhancing segmentation effects.  
Figure 9 presents two test images (Image 13 and Image 14), each of which is divided into 7 columns and 2 rows with a total of 14 images. (a) to (n) are the segmentation test results of the original test images, HRNet, SegNet, U-Net, U-Net + HAM-1, U-Net + HAM-2, U-Net + CBAM, U-Net + SE, MANet, MACU-Net, MAResU-Net, AttentionUNet, U-Net_scAG and DHAU-Net, respectively. In Image 13, the segmentation  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Methods\(\backslash\)EI** & **Accuracy** & **mIoU** & **Precision** & **Recall** & **F1 Score** \\ \hline SegNet & 0.9704 & 0.8639 & 0.8903 & 0.8391 & 0.8639 \\ SegNet + DHA & 0.9759 & 0.8884 & 0.9007 & 0.8817 & 0.8911 \\ \hline \end{tabular}
\end{table}
Table 4: Specific quantitative experimental results of transfer learning on the DHA module on the Massachusetts dataset.  
Figure 9: Visualization of experimental prediction results on the Inria Aerial Image Labeling Dataset. (**a**) Original, (**b**) HRNet, (**c**) SegNet, (**d**) U-Net, (**e**) U-Net + HAM-1, (**f**) U-Net + HAM-2, (**g**) U-Net + CBAM, (**h**) U-Net+SE, (**i**) MANet, (**j**) MACU-Net, (**k**) MAResU-Net, (**l**) AttentionUNet, (**m**) UNet_scAG, and (**n**) DHAU-Net.  
results of various methods of remote sensing images are significantly affected by noise and interference from roads, trees, cars, and shadows. Specifically, roads and trees are incorrectly segmented as buildings. U-Net, U-Net + HAM-1, U-Net + HAM-2, U-Net + SE, MANet, and MAResU-Net can accurately identify the buildings in the lower left corner, but there are also a small number of errors in dividing cars and roads into buildings. The segmentation results of U-Net + CBAM and DHAU-Net are comparable, but DHAU-Net is less disturbed by shadows. In Image 14, we explore the visual effect of large-area building coverage segmentation. HRNet, SegNet, MANet, MACU-Net, MAResU-Net, Attentio-nUNet, and U-Net_scAG do not completely segment large-area buildings, and exhibit a high error rate. Although U-Net + HAM-1 and U-Net + HAM-2 segmented most of the buildings, there were still a few missing segmentations. U-Net, U-Net + CBAM, U-Net + SE show overall better segmentation effects, and they can accurately and completely segment buildings, but they are not as effective as DHAU-Net in terms of detail retention. On the Inria Aerial Image Labeling Dataset, the addition of the DHA module to U-Net improves detail preservation. The DHA module can pay more attention to foreground buildings, ignore background interference information, and eliminate noise interference.