多尺度特征增强的ResUNet+遥感影像建筑物提取

Building Extraction of Remote Sensing Images Using ResUNet+with Enhanced Multiscale Features

针对ResUNet网络从遥感影像中提取小型和不规则建筑物时存在分割精度低和边界粗糙的问题,提出一种多尺度特征增强的残差U形网络ResUNet+。该网络以ResUNet网络结构为基础,在编码器内添加特征压缩激活模块以提升网络对有效特征的学习能力,在编码网络的最后一层使用空洞空间金字塔池化模块来获取不同尺度的建筑物上下文信息。在两个广泛公开使用的WHU航空图像数据集和INRIA建筑数据集上进行实验,并将其与SEUNet、DeepLabv3+、DenseASPP和ResUNet语义分割网络进行对比。实验结果表明,ResUNet+在精确率、召回率和F1分数3项精度指标中均表现最优,对测试影像中大小各异和形状不规则的建筑物具有更精确的分割结果。

We propose ResUNet+,an enhanced multiscale features residual Ushape network,to address issues in the extraction of small and irregular buildings from remote sensing images using the ResUNet,such as low segmentation accuracy and rough boundaries.Based on the ResUNet architecture,the squeeze and excitation module is used in the encoder to improve the network’s ability to learn effective features,and the atrous spatial pyramid pooling module is selected as the last layer of the encoding network to obtain context information of buildings at various scales.We evaluate the proposed ResUNet+and compare it with SEUNet,DeepLabv3+,DenseASPP,and ResUNet semantic segmentation networks on two commoly used public datasets:the WHU Aerial Imagery Dataset and INRIA Buildings Dataset.The results of the experiments show that ResUNet+outperforms other networks in terms of pecision,recall,and F_(1)-score.The segmentation results also show that RseUNet+excels at extracting buildings of various sizes and irregular shapes.