# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 5 Experiments
### Comparison with the State of the Art  
To verify the effectiveness of the proposed AFSF network, we conducted experiments on the new roof segmentation dataset and two common remote sensing semantic image segmentation datasets, i.e., the Inria Aerial Image Labeling (IAIL) [19] and WHU [20] datasets, and compare the results with several state-of-the-art methods, including Deeplab V3 [55], UNet [37], U2 Net [44], HED [56], RCF [57], BASNet [58], MA [42], AS-UNet++ [59], and MAD-UNet [60]. **Results on the new roof segmentation dataset:** We compare our AFSF network with several state-of-the-art methods. Table 4 illustrates the results of three evaluation metrics using seven methods on the new roof segmentation dataset. Our results show that the proposed AFSF network achieves the best performance on all three indicators. More specifically, the proposed AFSF network is 1.8%, 2.6%, and 3.1% higher than the best compared method, AS-UNet++ [59], in precision, recall, and IoU, respectively. These results highlight the effectiveness of the proposed AFSF network. It should be noted that both AS-UNet++ and MAD-UNet are based on UNet networks and both introduce attention mechanisms. We believe that, compared with AS-UNet++ and MAD-UNet, our method can better integrate features from all levels, which is important for semantic segmentation tasks. In addition, we also utilized more weakly labeled data to train the model, thereby improving its generalization ability. Figure 6 displays the segmentation results of the proposed AFSF and compared methods. **Result on the IAIL [19] and WHU [20] datasets:** To further validate the effectiveness of the proposed AFSF network, we compared the performance of our method with several state-of-the-art methods on the IAIL and WHU datasets in Tables 5 and 6. As we can see, our AFSF network achieves the best performance in all metrics on both of the two datasets. For example, on the IAIL dataset, the proposed AFSF network outperforms the second-best model, AS-UNet++ [59], by 0.7%, 0.7%, and 1.1% in terms of the precision, recall, and IoU, respectively; on the WHU dataset, the proposed AFSF network outperforms the second-best model, AS-UNet++ [59], by 0.6%, 0.5%, and 0.8% in terms of the precision, recall, and IoU, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline Deeplab V3 & 44.8 & 53.7 & 48.8 \\ UNet & 72.9 & 82.2 & 77.2 \\ U2 Net & 76.3 & 80.7 & 78.4 \\ HED [56] & 65.3 & 72.6 & 52.4 \\ RCF [57] & 70.5 & 81.3 & 60.7 \\ BASNet [58] & 80.2 & 85.4 & 70.6 \\ MA [42] & 81.1 & 85.6 & 71.3 \\ AS-UNet++ [59] & 81.5 & 86.3 & 72.4 \\ MAD-UNet [60] & 81.0 & 85.3 & 70.8 \\
**AFSF** & **83.3** & **88.9** & **75.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed AFSF network and several methods on the new roof segmentation dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline UNet & 83.1 & 81.1 & 69.7 \\ U2 Net & 84.8 & 81.5 & 71.1 \\ BASNet [58] & 86.3 & 83.2 & 73.5 \\ HED [56] & 82.5 & 80.2 & 68.5 \\ RCF [57] & 84.3 & 80.9 & 70.3 \\ MA [42] & 87.1 & 85.7 & 76.0 \\ AS-UNet++ [59] & 87.3 & 86.1 & 76.5 \\ MAD-UNet [60] & 86.6 & 85.2 & 75.3 \\
**AFSF** & **88.0** & **86.8** & **77.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed AFSF network and several methods on the IAIL dataset. Figure 6: Some examples of prediction masks generated by the proposed AFSF and compared methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline UNet & 94.6 & 90.7 & 86.2 \\ U2 Net & 95.0 & 91.2 & 87.0 \\ BASNet [58] & 95.9 & 92.9 & 89.3 \\ HED [56] & 94.2 & 90.4 & 85.6 \\ RCF [57] & 95.0 & 90.7 & 86.6 \\ MA [42] & 96.0 & 93.2 & 89.7 \\ AS-UNet++ [59] & 96.1 & 93.4 & 90.1 \\ MAD-UNet [60] & 95.9 & 93.0 & 89.6 \\
**AFSF** & **96.7** & **93.9** & **90.9** \\ \hline \hline \end{tabular}
\end{table}