针对经典深度学习语义分割网络对建筑物提取存在精度较低、边界模糊和小目标识别困难的问题,本文提出一种增强注意力门控的U型网络(advanced attention gate U-Net,AA_U-Net)用于改善建筑物提取的效果,该网络改进经典U-Net的结构,使用VGG16 作为主干特征提取网络、注意力门控模块参与跳跃连接、双线性插值法代替反卷积进行上采样.实验采用武汉大学建筑物数据集(WHU building dataset,WHD)对比提出的网络与部分经典语义分割网络的提取效果,并探究网络改进的各个模块对提取效果的影响.结果显示:该网络对建筑物提取的总精度、交并比、查准率、召回率和F1 分数分别为 98.78%、89.71%、93.30%、95.89%、94.58%,各项评价指标均优于经典语义分割网络,且改进的各个模块有效提高了提取精度,改善了建筑物轮廓不清晰和小目标建筑物破碎的问题,可用于精准提取高分辨率遥感影像中的建筑物信息,对城市规划、土地利用、生产生活、军事侦察等具有指导意义.
Building extraction based on advanced attention gate U-Net
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.
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