MAFF-HRNet: Multi-Attention Feature Fusion HRNet for Building Segmentation in Remote Sensing Images

Abstract

Built-up areas and buildings are two main targets in remote sensing research; consequently, automatic extraction of built-up areas and buildings has attracted extensive attention. This task is usually difficult because of boundary blur, object occlusion, and intra-class inconsistency. In this paper, we propose the multi-attention feature fusion HRNet, MAFF-HRNet, which can retain more detailed features to achieve accurate semantic segmentation. The design of a pyramidal feature attention (PFA) hierarchy enhances the multilevel semantic representation of the model. In addition, we develop a mixed convolutional attention (MCA) block, which increases the capture range of receptive fields and overcomes the problem of intra-class inconsistency. To alleviate interference due to occlusion, a multiscale attention feature aggregation (MAFA) block is also proposed to enhance the restoration of the final prediction map. Our approach was systematically tested on the WHU (Wuhan University) Building Dataset and the Massachusetts Buildings Dataset. Compared with other advanced semantic segmentation models, our model achieved the best IoU results of 91.69% and 68.32%, respectively. To further evaluate the application significance of the proposed model, we migrated a pretrained model based on the World-Cover Dataset training to the Gaofen 16 m dataset for testing. Quantitative and qualitative experiments show that our model can accurately segment buildings and built-up areas from remote sensing images.