基于Res_AttentionUnet的高分辨率遥感影像建筑物提取方法

针对目前基于深度学习与高分辨率遥感影像的建筑物提取研究现状,本文提出了一种综合ResNet中的ResBlock残差模块和Attention注意力机制的改进型Unet网络（Res_AttentionUnet）,并将其应用于高分辨率遥感影像建筑物提取,有效地提高了建筑物的提取精度。具体优化方法为：在传统的Unet语义分割网络卷积层中加入针对初高级特征加强提取的ResBlock残差模块,并在网络阶跃连接部分加入Attention注意力机制模块。其中,ResBlock残差模块使卷积后的特征图获取更多的底层信息,增强卷积结构的鲁棒性,从而防止欠拟合;Attention注意力机制可增强对建筑物区域像素的特征学习,使特征提取更完善,从而提高建筑物提取的准确率。本研究采用武汉大学季顺平团队提供的开放数据集（WHU Building Dataset）作为实验数据,并从中选取3个具有不同建筑物特征和代表性的实验区域,然后分别对不同实验区域进行预处理（包括滑动裁剪和图像增强等）,最后分别使用Unet、ResUnet、AttentionUnet和Res_AttentionUnet 4种不同的网络模型对3个不同实验区进行建筑物提取实验,并对实验结果进行交叉对比分析。实验结果表明,与其他3种网络相比,本文所提出的Res_AttentionUnet在基于高分辨率遥感影像的建筑物提取中具有更高的精度,平均提取精度达到95.81%,相较于原始Unet网络提升17.94%,同时相较于仅加入残差模块的Unet网络（ResUnet）提升2.19%,能够显著地提升高分辨率遥感影像中建筑物提取的效果。

Building Extraction from High-Resolution Remote Sensing Image based on Res_AttentionUnet

To contribute to the current research of building extraction based on deep learning and high-resolution remote sensing images, we propose an improved Unet network (Res_AttentionUnet), which combines the Residual module of ResNet and Attention mechanism. We apply the Unet network to the extraction of buildings from high-resolution remote sensing images, which effectively improves the extraction accuracy of buildings. The specific optimization method can be divided into three parts. Firstly, in the traditional Unet semantic segmentation network convolution layer, the ResBlock module is added to enhance the extraction of low-level and high-level features. Meanwhile, the Attention mechanism module is added to the network step connection part. Secondly, in the whole net, the ResBlock module enables the convoluted feature map to obtain more bottom information and enhance the robustness of the convolution structure, so as to prevent underfitting. Thirdly, the Attention mechanism can enhance the feature learning of building area pixels, making feature extraction more complete, so as to improve the accuracy of building extraction. In this study, we use the open data set (WHU Building Dataset), provided by Ji Shunping team of Wuhan University, as the experimental data and select three experimental areas with different building characteristics and representativeness. Then, we preprocess the different experimental areas (including sliding, cropping, and image enhancement, etc.). Finally, we use four different network models of Unet, ResUnet, AttentionUnet, and Res_AttentionUnet to extract buildings from three different experimental areas. The experimental results are cross-compared and analyzed. The experimental results show that, compared with the other three networks, the Res_AttentionUnet proposed in this paper has higher accuracy in the building extraction from high-resolution remote sensing images. The average extraction accuracy of Res_AttentionUnet is 95.81%, which is 17.94% higher than the original Unet network, and 2.19% higher than ResUnet (the Unet with only residual module). The results demonstrate that Res_AttentionUnet can significantly improve the effectiveness of building extraction in high-resolution remote sensing images.