全球定位系统2024，Vol.49Issue(2)：43-53,11.DOI:10.12265/j.gnss.2023175
基于增强注意力门控U-Net的建筑物提取研究
Building extraction based on advanced attention gate U-Net
摘要
Abstract
To facilitate the problems of low accuracy,fuzzy boundary,and difficulty in identifying small targets in building extraction using deep learning semantic segmentation networks,we propose an advanced attention gate U-Net(AA_U-Net)to improve the effect of building extraction.This network improves the structure of classic U-Net,using VGG16 as the backbone feature extraction network,attention-gated module participating in skip connection,and bilinear interpolation instead of deconvolution for upsampling.In the experiment,we use the Wuhan University building dataset(WHD)to compare the extraction effect of the proposed network and some classical semantic segmentation networks and explore the influence of each module of the network improvement on the extraction.The results show that the total accuracy,intersection of union,precision,recall rate,and F1 score of the network are 98.78%,89.71%,93.30%,95.89%,and 94.58%,respectively.All evaluation indexes are better than the classical semantic segmentation network,and the improved modules can effectively improve the extraction accuracy.The problem of unclear outlines of buildings and fragmentation of small target buildings was improved,too.It can be used to accurately extract building information from high-resolution remote sensing images,which has guiding significance for urban planning,land use,production,life,and military reconnaissance.关键词
高分辨率遥感影像/深度学习/语义分割/增强注意力门控U-Net/建筑物提取Key words
high-resolution remote sensing images/deep learning/semantic segmentation/advanced at-tention gate U-Net/building extraction分类
天文与地球科学引用本文复制引用
任远锐,陈朋弟,高小龙..基于增强注意力门控U-Net的建筑物提取研究[J].全球定位系统,2024,49(2):43-53,11.基金项目
甘肃省自然资源科技项目(202223)感谢兰州大学高性能计算平台与甘肃省自然资源科技项目的支持. （202223）
