ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image

Aimed at the challenge of low accuracy of building segmentation caused by poor continuity of remote-sensing-image regions and blurred boundaries, a remote sensing building semantics segmentation algorithm based on multiscale regional consistent attention supervision is proposed. First, based on the Unet encoder–decoder architecture, the proposed algorithm constructs the region attention network (ReA-Net), which employs a multiscale receptive field-guidance model to simultaneously focus on regional features and edge details of remote sensing image objects. Second, the self-attention mechanism is employed to establish the correlation representation of regional-level features of remote sensing images, and multiscale regional attention features of remote sensing images are obtained through weighted regional-level correlation mapping. Finally, to address the lack of spatial correlation constraints on the prediction of remote sensing images segmentation, a loss function with multiscale neighborhood consistency supervision is suggested to constrain the consistency of pixel label assignment related to a local region. Experimental results on WHU building dataset showed that intersection over union (IOU) reached 91.6%, precision reached 95.61%, recall reached 95.68% recall, and F1-score reached 95.64%; On the Massachusetts building dataset, IOU reached 74.77% and precision reached 83.93%, recall reached 87.53%, and F1-score reached 85.69%. Therefore, the proposed algorithm not only has a good segmentation effect but also has a strong robustness for remote sensing building image segmentation.