A Multi-Scale attention network for building extraction from high-resolution remote sensing images.

The information in remote sensing images often leads to incomplete building contours and suboptimal adaptability to complex building scenes. To address these issues, we propose a novel multi-scale network with dual attention mechanisms to extract clear building boundaries. The Squeeze-and-Excitation (SE) module is employed to bolster feature extraction, and the Atrous Spatial Pyramid Pooling (ASPP) module is integrated to capture multi-scale feature information. Then, in the decoding phase, channel grouping shuffle and dual attention mechanisms are synergistically integrated to exploit the interrelations and global dependencies of building features. Finally, a hybrid loss function is devised to address the class imbalance and thereby ensure more stable network training. Experimental evaluations on two high-resolution remote sensing datasets, Zimbabwe and Massachusetts, demonstrate that the proposed method markedly surpasses the performance of semantic segmentation networks such as PSPnet, U-net, and DAnet in terms of accuracy, recall, F1 score, and Mean Intersection over Union (MIoU), achieving an F1 score of up to 83.23% and an MIoU of 73.56%. This multi-scale attention network holds substantial promise for practical applications in building extraction.