Method for Building Extraction from Remote Sensing Images Based on Non-local Attention and Multi-layer Residuals
With the development of urbanization and remote sensing technology,the tasks of extracting objects from high-resolution remote sensing images have become increasingly challenging.To address the limitation in existing methods,e.g.,the inability to capture long-range spatial relationships and false positives and negatives in remote sensing images,in this paper,a method for building extraction from remote sensing images based on non-local attention and milti-layer residuals is proposed,which is also called the non-local attention guided multi-layer residual net(NAMR-Net).Built upon the refined U-Net architecture,the NAMR-Net incorporates an adaptive non-local attention block(ANAB)and a multi-layer residual learning block(MRLB).Consequently,the network can integrate features from distant pixels at different convolutional layers,and effectively enhance the segmentation quality of buildings through a two-stage training process.Experiments are conducted on two publicly available datasets,i.e.,WHU and Massachusetts.The results demonstrate that the NAMR-Net achieves high-quality segmentation of building targets in remote sensing images and outperforms several state-of-the-art methods.
high resolution remote sensing imagebuilding extractiondeep learningresidual learningnon-local attention
国家科技期刊平台
NSTL
万方数据
