Building extraction based on advanced attention gate U-Net
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摘要: 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.
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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
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. -
图 1 U-Net结构
注：每个方框对应一个多通道特征图，棕红色代表编码器部分特征图，粉紫色代表解码器部分特征图；通道的数量表示在方框的顶部；特征图尺寸表示在方框下边缘；箭头表示不同的操作.
图 2 本文提出的网络结构
注：每个方框对应一个多通道特征图，棕红色代表编码器特征图，青色代表VGG16比U-Net编码器增加的特征图，土黄色代表经注意力门控模块运算之后复制的特征图，红色代表解码器部分特征图；通道的数量表示在方框的顶部；特征图尺寸表示在方框下边缘处；箭头表示不同的操作.
图 3 U-Net编码器与VGG16主干网结构对比
注：方框及颜色含义与图2相同；(a)代表U-Net的编码器部分，(b)代表VGG16的主干特征提取部分；字体为红色表示VGG16与U-Net的特征图通道数差异.
表 1 不同网络分割精度对比
% 网络 IoU Precision Recall F1 OA U-Net 87.41 91.46 95.18 93.28 98.47 SegNet 87.13 91.94 94.34 93.12 98.45 FCN 69.15 85.03 78.73 91.76 96.09 DeepLabV3 68.89 82.38 80.79 81.58 95.94 DeepLabV3+ 79.31 88.27 88.65 88.46 97.43 PSPNet 79.80 85.13 92.72 88.76 97.39 AA_U-Net 89.71 93.30 95.89 94.58 98.78 
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表 2 不同主干网提取精度对比
% 网络 IoU Precision Recall F1 OA ResNet50 85.17 89.98 94.09 91.99 98.18 MobileNetV3 83.93 90.00 92.56 91.27 98.03 VGG16 89.16 93.29 95.27 94.27 98.71
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表 3 解码器改进前后分割精度对比
% 网络状态 IoU Precision Recall F1 OA Before 89.43 93.42 95.44 94.42 98.74 After 89.71 93.30 95.89 94.58 98.78
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表 4 消融各模块分割精度对比
网络 VGG AG BIU IoU/% Precision/% Recall/% F1/% OA/% U-Net - - - 87.41 91.46 95.18 93.28 98.47 VGG 有 - - 89.16 93.29 95.27 94.27 98.71 VGG+AG 有 有 - 89.43 93.42 95.44 94.42 98.74 VGG+AG
+BIU有 有 有 89.71 93.30 95.89 94.58 98.78
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