带洞型U-Net++网络在遥感影像中建筑物的提取方法

A Method for Extracting Buildings from Remote Sensing Images with Hole U-Net++ Network

针对目前高分辨率遥感建筑物提取分割尺度失真和边界不完整等问题,提出了一种带洞型U-Net++网络模型用于建筑物语义分割的方法。该方法顾及全卷积网络在下采样提取特征的过程中损失的遥感影像细节特征,通过必要的长连接和短连接还原了下采样带来的信息损失,利用空洞卷积使模型能够适应不同尺度的遥感影像建筑物提取。在Massachusetts建筑物数据集上的实验结果表明,该方法能够更完整提取遥感影像中的建筑物信息,在测试集上准确率达到了89.37%,平均交并比(intersection over union,IoU)达到了72.35%。

In view of the current problems of high-resolution remote sensing building extraction and segmentation,such as scale distortion and boundary incompleteness,we propose a U-Net++network model with holes for semantic segmentation of buildings.This method takes into account the detail features of remote sensing images lost in the process of extracting features by downsampling based on full convolutional network(FCN).It restores the information loss by downsampling through long and short connections.The model can adapt to building extraction at different scales by dilated convolution.The experimental results on the Massachusetts building dataset show that,this method can extract building information from remote sensing images more completely,and its accuracy rate on the test set reaches 89.37%,and the mean intersection over union(IoU)reached 72.35%.