Abstract: Building extraction from remote sensing images based on deep learning has the characteristics of wide coverage and high computational efficiency,and it plays an important role in urban construction,disaster prevention and other aspects.Most of the mainstream methods use multi-scale feature fusion to enable the neural network to learn more abundant semantic information.However,due to the complexity of multi-scale features and the interference of other ground objects,this kind of methods often lead to target missing and noise-intensive.To this end,this paper proposes a feature interpretation model MGA-ResNet50(MGAR) that combines attention mechanism.The core of the method is to use the multihead attention to process the hierarchical weighting of high-level semantic information,so as to extract the optimal feature combination with relatively better representation effect.Then use the gating structure to fuse the feature map of each dimension with the low-level semantic information of the corresponding encoder to compensate for the loss of local building details.Experimental results on public datasets such as Massachusetts Building and WHU Building show that the proposed algorithm can achieve higher F1 and IoU than the more advanced multi-scale feature fusion methods such as RAPNet,GAMNet and GSM.

Key words: Deep learning, Building extraction, Multi-scale feature, Multihead attention, Gating mechanism