摘要
Abstract
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.关键词
高分辨率遥感图像/建筑物提取/深度学习/残差学习/非局部注意力Key words
high resolution remote sensing image/building extraction/deep learning/residual learning/non-local attention分类
信息技术与安全科学
