Building extraction from remote sensing images based on deep learning faces two challenges:incomplete extraction with large coverage and the loss of partial boundary details.To address these issues,a novel network model HFRA-Net using hybrid feature fusion and residual attention mechanism is proposed.The model introduces a hybrid feature fusion module to capture global and local features in a parallel structure,enhancing the integrity of building segmentation.At the same time,the residual attention mechanism of top-down and bottom-up bidirectional attention feedback is introduced to adaptively capture the boundary detail information at different scales.Finally,experimental results on two public datasets demonstrate the effectiveness of the proposed method for building extraction.
NETL
NSTL
万方数据
