摘要
为了克服传统的U-Net网络高分辨率遥感影像建筑物提取可能出现的漏检,以及边缘细节损失的问题,以U-Net网络作为基础模型,提出一种多尺度采样模块(residuals elan block)以及多分支组合下采样模块结合的语义分割算法。通过重新设计网络模型的编码器,使网络编码获取更多语义信息;通过采用卷积与池化结合的方法改善池化带来的空间信息丢失问题。在WHU Building Dataset公开数据集上的实验结果表明,该算法的精准度为85.01%,交并比为80.88%,比基础模型算法分别提高了4.73%和10.6%。
In order to overcome the possible problems of missing detection and edge detail loss in building extraction of highresolution remote sensing images using the traditional U-Net network,a semantic segmentation algorithm combining the multi-scale sampling module(residuals elan block)and the multi-branch sub-sampling module is proposed based on the U-Net network as the basic model.By redesigning the encoder of the network model,the network coding can obtain more semantic information.By combining convolution and pooling,the spatial information loss caused by pooling can be improved.The experimental results on the open data set of WHU Building Dataset show that the algorithm's precision is 85.01%and the IoU is 80.88%,which is 4.73%and 10.6%higher than the basic model algorithm,respectively.
作者
李松宇
LI Songyu(College of Information Science and Technology,Bohai University,Jinzhou 121013,China)
出处
《现代信息科技》
2023年第11期89-92,96,共5页
Modern Information Technology
