## 3 Experiments and Results
### Experimental Results  
#### 3.4.1 Results with the Massachusetts Buildings Data Set  
Figure 9 lists the building extraction results for the Massachusetts buildings data set predicted by the U-Net, UNet ++, SegNet, DeeplabV3+ and AEUNet++, respectively. Seen from the segmentations of U-Net, SegNet and DeeplabV3+, due to mixed pixels and spectral similarity in the image, they produced prediction results including an amount of salt-and-pepper noise and showed weaker performance compared with the other two methods. Additionally, many mislabeled pixels were produced; for example, many road pixels were labeled as building pixels in marked areas A and E, some building pixels were mislabeled as background in marked areas D and F because the plain skip connections are often used in U-Net for fusing multiscale features, and many noise features were extracted by SegNet and DeeplabV3+, which may result in insufficient fusion of feature maps from different layers. Seen from marked areas A, C, D and F, UNet ++ and AEUNet++ presented more homogeneous segmentation maps with accurate boundaries due to fine-grained details and features captured by the nested and dense skip connections introduced in UNet++ and AEUNet++. Compared with UNet++, AEUNet++ produced more homogeneous segmentation maps with accurate boundaries due to the introduce of attention block and multi-task learning to adaptively fuse multiscale features and edge constraints. Although UNet++ nearly eliminated most of the isolated noises and obtained satisfactory results, a number of details in the image content were missing, and many pixels were mislabeled. As we can see from the segmentation results obtained with AEUNet++, most of the isolated noises were eliminated and details of the image content were satisfactorily preserved. Taking the marked area E as an example, many road pixels were mislabeled as building pixels by UNet++. Seen from the marked area F, many shadow pixels were mislabeled as building pixels by UNet++, while in AEUNet++, the pixels were mostly correctly labeled. The main reasons may be as follows: in the traditional UNet++, the total loss was obtained by averaging the final output of extracted feature maps by UNet++; the simple averaged result may suppress the better feature maps and raise the negative feature maps from the final output. However, in AEUNet++, the four prediction feature maps were adaptively refined through the attention block, and as a result, feature maps were learned that should have been emphasized or suppressed. As we can see from Figure 8, AEUNet++ achieved the most accurate boundaries among the three methods, because in the three methods, progressive down-sampling may cause the loss of location information, which will result in poor boundaries. The multi-task learning was introduced into the proposed AEUNet++ network to incorporate building boundary geometric properties, and as a result, more accurate boundaries will be preserved in comparison with U-Net, UNet++, SegNet and DeeplabV3+.  
Table 1 gives the quantitative evaluation results. Seen from Table 1, UNet++ and AEUNet++ yielded higher segmentation accuracies than that of U-Net. Amongst the three models, AEUNet++ obtained the greatest accuracy. Compared with UNet++, after adding the attention block and multi-task learning training, the prediction accuracy was significantly improved; AEUNet++ obtained 95.12%, 83.40%, 66.10%, 73.75%, 58.41%, and 76.58% with respect to OA, Precision, Recall, F1, IoU, and mIoU index, respectively. The  
accuracy gains with AEUNet++ over UNet++ were 0.37%, 1.78%,2.32%, 2.14%, 2.64% and 1.51%, respectively, yielding improvements of approximately 2.64%, 2.0%, 0.97% and 0.11% IoU compared with the other four methods.