## 3 Experiments and Results
### Experimental Results
#### 3.4.2 Results with the WHU Aerial Image Data Set  
Figure 10 illustrates the segmentation results based on the WHU aerial image data set using U-Net, UNet ++, SegNet, DeeplabV3+ and AEUNet++, respectively. Visually, UNet++ and DeeplabV3+ produced more homogeneous segmentation maps with accurate boundaries than those of U-Net and SegNet, and AEUNet++ had the best performance, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **OA** & **Precision** & **Recall** & **F1** & **IoU** & **mIoU** \\ \hline U-Net & 94.75 & 81.62 & 63.78 & 71.61 & 55.77 & 75.07 \\ UNet++ & 94.82 & 81.68 & 64.58 & 72.13 & 56.41 & 75.43 \\ SegNet & 94.89 & 80.99 & 66.39 & 72.97 & 57.44 & 75.98 \\ DeeplabV3+ & 93.97 & 81.11 & **67.40** & 73.62 & 58.30 & 75.64 \\ AEUNet++ & **95.12** & **83.40** & 66.10 & **73.75** & **58.41** & **76.58** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of OA, Precision, Recall, F1, IoU and mIoU for the Massachusetts buildings data set. Figure 9: Segmented patches from U-Net, UNet++, SegNet, DeeplabV3+ and AEUNet++ based on the Massachusetts buildings dataset. The white color in the segmented patches represents pixels belonging to buildings (A–F denote the marked areas). whereas extensive salt-and-pepper noises existed in the U-Net, SegNet and DeeplabV3+ results. UNet++ enhanced the U-Net to some extent, but showed weaker performance than AEUNet++. This was also illustrated in areas A-H. Specially, many small areas of buildings were mislabeled as background by U-Net, UNet++, SegNet and DeeplabV3+ in areas A, B, C and F, but AEUNet++ showed better performance. The main reason may be that insufficient utilization of feature maps from different layers may have occurred due to the use only of plain skip connections by U-Net. Though UNet++ used the dense skip connections to capture more representative feature maps, negative feature maps were produced. Moreover, in order to increase the perceptions of U-Net and UNet++, many max-pooling operators were applied, which may have ignored the details of objects and location information. Compared with U-Net, UNet++, SegNet and DeeplabV3+, AEUNet++ introduced the attention block for adaptively refining feature maps extracted by UNet++ according to their contributions, and the multi-task learning was introduced to incorporate building boundary geometric information for guiding accurate boundaries. As also illustrated in Table 2, AEUNet++ achieved the greatest accuracy of 98.97%, 95.23%, 95.43%, 95.33%, 91.08%, and 94.97%, respectively, for OA, Precision, Recall, F1, IoU, and mIoU index. In particular, compared with UNet++, Precision and IoU increased by 1.92% and 3.24%, respectively, and AEUNet++ obtained IoU improvements of approximately 6.27%, 3.24%, 2.6% and 0.15% over U-Net, UNet++, SegNet and DeeplabV3+, respectively. Figure 10: Segmented patches from U-Net, UNet++, SegNet, DeeplabV3+ and AEUNet++ based on the WHU aerial image data set. The white color in the segmented patches represents pixels belonging to buildings (A–H denote the marked areas). From the above two experiments, we can draw conclusions from Figures 8 and 9 for the two data sets. Whether for the dense and small scale of building distributions that required smaller and narrower receptive fields, or for the sparse and large scale of building distributions that required larger and wider receptive fields, the AEUNet++ model exhibited very good robustness to building information that could be accurately extracted under different scales and different distributions. Comparing the prediction results from Figures 8 and 9, AEUNet++ produced better performance based on the WHU aerial image data set than that based on the Massachusetts buildings data set, because the resolution of the WHU aerial image was higher than that of the Massachusetts image. Too many mixed pixels existed in the Massachusetts buildings data set, which may have resulted in too many mislabeled pixels in the prediction results.