# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 4 Results and Analysis
### Massachusetts Dataset
#### 4.1.2 Comparison Experiment for the Attention Module  
To further illustrate the advantages of the DHA module, we compare it with other attention modules, namely CBAM and squeeze-and-excitation (SE), when integrated into the U-Net architecture. We explore the contributions of the DHA module and other attention modules to the performance of urban remote sensing building image segmentation. The training is conducted under the same experimental conditions, and the training parameters are unified. All networks are trained from scratch without using pretrained models. The training results of our proposed DHA module are superior to those of the existing comparison models. The specific quantitative experimental results are shown in Table 3, and a visual comparison of the network effects is shown in Figure 6.  
Table 3 shows that the U-Net + CBAM does not perform well across various metrics, including accuracy, mIoU, precision, recall, and F1 score. This indicates that the CBAM is not suitable for use in the skip connections of U-Net to process urban remote sensing building image segmentation tasks. The results obtained with the U-Net + SE are slightly worse than our DHAU-Net in all metrics, which further confirms that our DHA module has a strong effect on urban remote sensing building image segmentation.  
Figure 6 shows a total of three prediction plots. Each image is divided into four columns. Column (a) is the original image, and columns (b) to (f) correspond to the visualization of the prediction results produced by the U-Net + CBAM, U-Net + SE, U-Net + HAM-1, U-Net + HAM-2 and DHAU-Net architectures.  
Figure 6 provides visualization results of the DHA module and the two compared attention modules (the CBAM and SE modules). In Image 7, both U-Net + SE and DHAU-Net achieve comparable accuracy for building segmentation, but DHAU-Net performs better in terms of maintaining the integrity of the building segmentation in certain aspects. All three methods exhibit a strong performance in handling the noise interference, underscoring the effectiveness of spatial and channel attention mechanisms in this regard. In cases involving complex environmental interference, such as sparse buildings and images with pronounced shadow effects as shown in Image 8, U-Net + CBAM and U-Net + SE are still better in terms of accuracy but fail to deliver satisfactory building integrity. In Image 9, where buildings are denser and more evenly distributed, three methods exhibit excellent segmentation effects. However, U-Net + CBAM and U-Net + SE tend to produce more confusing building segmentation results. On the whole, our DHAU-Net not only performs well in cases involving sparse and dense buildings but also yields more accurate segmentation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods\textbackslash{}EI & **Accuracy** & **mIoU** & **Precision** & **Recall** & **F1 Score** \\ \hline U-Net + CBAM & 0.9525 & 0.7876 & 0.8378 & 0.7132 & 0.7705 \\ U-Net + SE & 0.9763 & 0.8885 & 0.9204 & 0.8631 & 0.8909 \\ U-Net + HAM-1 & 0.9725 & 0.8721 & 0.9037 & 0.8426 & 0.8720 \\ U-Net + HAM-2 & 0.9785 & 0.8984 & 0.9236 & 0.8799 & 0.9012 \\ DHAU-Net & 0.9808 & 0.9088 & 0.9300 & 0.8932 & 0.9112 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specific quantitative experimental results of ablation learning on attention mechanisms on the Massachusetts dataset.  
results when the given buildings are seriously disturbed by noise and shadows. The ability of DHAU-Net's to enhance information screening and reinforce skip connections plays a crucial role in achieving superior segmentation outcomes, particularly in challenging conditions with noise and complex environmental disturbances.