# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 4 Results and Analysis
### Massachusetts Dataset  
#### 4.1.1 Model Comparison Experiment  
To evaluate the performance of DHAU-Net, this paper conducts comprehensive experiments on the Massachusetts Remote Sensing Building Image dataset. We selected three existing state-of-the-art deep learning methods (i.e., SegNet, U-Net and HRNet) and 5 advanced neural networks (i.e., MANet, MACU-Net, MAReU-Net, AttentionUNet, and UNet_scAG) are selected for comparison under the same experimental setting. Each network was trained from scratch without using pre-trained models, and all networks converged during training, as illustrated in the iterative mIoU training plot in Figure 3. Our proposed DHAU-Net are superior to the existing comparison models. The specific quantitative experimental results are presented in Table 2, and the visual comparison effects are shown in Figures 4 and 5. From Table 2, DHAU-Net achieves a higher performance across various evaluation metrics compared to the comparison methods. Specifically, for DHAU-Net, the accuracy rate (Accuracy), precision rate (Precision), recall rate (Recall), F1 score and mIoU are 0.9808, 0.9300, 0.8932, 0.9112, and 0.9088, respectively. This indicates the effectiveness of the proposed DHAU-Net in enhancing information screening and reinforcement through skip connections. On the basis of retaining the original feature information provided by the skip connections, different pieces of spatial and channel information are further extracted from the shallow network. This increases the edge detail information and anti-noise ability of the network for building segmentation and reduces the misjudgments and missed classifications regarding large-area buildings in remote sensing images. The five evaluation metrics of HRNet which performs the multi-scale fusion via repeated cross-parallel convolution, and SegNet which uses pooling indices, are lower than those of our method. U-Net achieves a slightly lower performance compared to our method, with a difference of 0.0272, 0.0395, and 0.0279 in mIoU, recall, and F1 score, respectively. This illustrates the effectiveness of the enhancements introduced in this paper, leveraging the U-Net architecture. The improvements contribute to enhanced building segmentation results in urban remote sensing images. Figure 3 shows the mIoU curves obtained during the training process of HRNet, SegNet, U-Net, MANet, MACU-Net, MAResU-Net, AttentionUNet, UNet_scAG and DHAU-Net approaches. The graph illustrates the convergence behavior of the nine methods, showing that all approaches achieve stability without significant fluctuations. This indicates that the selected loss function, training optimizer, and parameter configurations are suitable for the training process of each method. The smooth and consistent nature of the curves suggests that the models effectively learn from the training data and converge towards optimal solutions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods\textbackslash{}EI** & **Accuracy** & **mIoU** & **Precision** & **Recall** & **F1 Score** \\ \hline HRNet & 0.9688 & 0.8552 & 0.6493 & 0.6254 & 0.6371 \\ SegNet & 0.9704 & 0.8639 & 0.8903 & 0.8391 & 0.8639 \\ U-Net & 0.9748 & 0.8816 & 0.9152 & 0.8536 & 0.8833 \\ MANet & 0.9799 & 0.9051 & 0.9252 & 0.8926 & 0.9086 \\ MACU-Net & 0.9778 & 0.8956 & 0.9191 & 0.8789 & 0.8985 \\ AttentionUNet & 0.9740 & 0.8791 & 0.9064 & 0.8564 & 0.8807 \\ MAResU-Net & 0.9707 & 0.8652 & 0.8897 & 0.8425 & 0.8655 \\ UNet\_scAG & 0.9799 & 0.9057 & 0.9161 & 0.9028 & 0.9094 \\ DHAU-Net & 0.9808 & 0.9088 & 0.9300 & 0.8932 & 0.9112 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific quantitative experimental results of DHAU-Net and comparative methods on the Massachusetts dataset. Figure 3: mIoU curves of HRNet, SegNet, U-Net, e MANet, MACU-Net, MAResU-Net, AttentionUNet, UNet_scAG and DHAU-Net on the Massachusetts dataset.