江西科学2024，Vol.42Issue(2)：355-359,5.DOI:10.13990/j.issn1001-3679.2024.02.021
基于UNet模型的遥感影像建筑物变化检测研究
Research on Building Change Detection in Remote Sensing Image Based on UNet Model
摘要
Abstract
UNet is a typical symmetric U-shaped network,for the problem that this network cannot accurately capture the boundary and detail information of buildings.In this paper,we propose an improved UNet network model,we add the scSE attention module,which can improve the network perception,to the jump link of UNet network model,and at the same time,we replace the encoder in the model with VGG19,which is able to better capture the image details and textures,to conduct building change detection experiments on the publicly available dataset LEVIR-CD.The experimen-tal results show that the method improves the recall by 13.18%and F1 by 5.17%compared to the original method although the precision rate decreases by 0.66%.This shows that the method effec-tively improves the detection of building boundaries and details by the UNet network model,so that the precision of building change detection is effectively improved.关键词
建筑物变化检测/注意力机制/UNet/遥感影像/编码器Key words
building change detection/attention mechanism/UNet/remote sensing image/encoder分类
天文与地球科学引用本文复制引用
王盼盼,刘超,孙健飞,樊亚,刘佳祥,董亮..基于UNet模型的遥感影像建筑物变化检测研究[J].江西科学,2024,42(2):355-359,5.基金项目
安徽省高等学校科学研究项目(2022AH050849). （2022AH050849）
