# 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
In Figures 4 and 5, we present visual comparisons of the prediction results from different methods, highlighting various aspects such as the noise interference, uniform building distributions, dense building distributions, and complex urban environments. The visual analysis is conducted to showcase the superior segmentation performance of the proposed method. This is evidenced by the method's effectiveness in retaining building edge contours, reducing misclassifications, and minimizing instances of missed classifications. Figures 4 and 5 show a total of six prediction plots. Each image consists of five columns. Column (a) is the original image, and columns (b) to (j) correspond to visualizations of the prediction results provided by the HRNet, SegNet, U-Net, MANet, MACU-Net, MAResU-Net, AttentionUNet, UNet_scAG and DHAU-Net, respectively. By comparing the visual outputs, we can observe the effectiveness of the proposed method in accurately segmenting buildings and producing detailed and precise predictions across various challenging scenarios. Figure 4 mainly shows the image segmentation effects achieved by adding random noise to the remote sensing image. As shown in the red box in Image 1, the segmentation  
Figure 4: Visualization of the experimental prediction results of DHAU-Net and contrastive methods on images with random noise on the Massachusetts dataset. (**a**) Original, (**b**) HRNet, (**c**) SegNet, (**d**) U-Net, (**e**) MANet, (**f**) MACU-Net, (**g**) MAResU-Net, (**h**) AttentionUNet, (**i**) UNet_scAG, and (**j**) DHAU-Net. results of HRNet and SegNet have obvious missing points. Although U-Net can recognize the existence of buildings, its segmentation results are not as complete as those of our proposed method. In Image 2, due to the noise interference in the test images, the segmentation results of HRNet and SegNet are visually confusing, and the segmentation accuracy is insufficient. Our DHAU-Net weakens the interference of irrelevant information by adding a channel attention mechanism in the skip connections so that the segmented images have stronger anti-interference ability. In Image 3, despite a large area of grass that is contained in the image, DHAU-Net still performs well by accurately segmenting the building without misclassifying the grass as a building. In contrast, HRNet, SegNet, and U-Net have obviously incorrect segmentation results, as shown in the red boxes in Image 3. It demonstrates the robustness and accuracy of the proposed DHAU-Net in handling noise interference, preserving building details, and avoiding misclassifications in complex urban environments. Figure 5 shows some urban remote sensing image segmentation results obtained in more complex environments. In Image 4 and Image 6, there are many trees, gray grassy areas, and concrete road disturbances. Our DHAU-Net can accurately remove these dis  
Figure 5: Visualization of the experimental prediction results of DHAU-Net and contrastive methods on images with complex environments on the Massachusetts dataset. (**a**) Original, (**b**) HRNet, (**c**) SegNet, (**d**) U-Net, (**e**) MANet, (**f**) MACU-Net, (**g**) MAResU-Net, (**h**) AttentionUNet, (**i**) UNet_scAG, and (**j**) DHAU-Net. turbances as well as keep the edge details of the building segmentation intact. In contrast, the segmentation results of HRNet, SegNet, and U-Net have some missing and misclassified buildings, as highlighted by the red boxes in Image 4 and Image 6. There is an obvious interference situation and a large sports field presented in the green box in Image 5, which HRNet mistakenly identifies as a building. Overall, the segmentation results of our proposed method are relatively complete, while SegNet and U-Net exhibit noticeable misclassifications, and their segmentation results for larger buildings lack completeness. The DHAU-Net effectively handles complex environments by accurately segmenting buildings and removing unwanted interference, showcasing its superior performance compared to the other methods.