Semantic Segmentation of Urban Buildings from VHR Remotely Sensed Imagery Using Attention-Based CNN

With the emergence of large scale Very High Resolution (VHR) remotely sensed imageries, more accurate urban building segmentation now become possible. Meanwhile, various appearances along with complicated background of the urban VHR imageries make accurate segmentation of urban buildings yet a very challenging task. In this study, we proposed DeepAttentionUnet, which is an end-to-end attention-based model following the basic structure of U-Net and being integrated with attention mechanism so that the model can automatically learn to focus on target building structure of different sizes and shapes. This model adopts attention mechanism to implicitly highlight useful features of buildings while suppressing irrelevant areas of input images and combines with residual learning method to ease training process by alleviating the degradation problem in the training process that often occurs in such applications. Our proposed DeepAttentionUnet was tested with a set of 0.075m resolution aerial images and was compared for performance under the exact same conditions with other two state-of-the-art segmentation networks, i. e. SegNet and U-Net, respectively. The experiment results suggested that in both quantitative and visual evaluation, our proposed model outperformed other two models in urban building semantic segmentation tasks in terms of F1 score, Kappa coefficient and overall accuracy, moreover, it also takes the advantage of having much less parameters compared with other two state-of-art models in comparison.