Research on Building Change Detection in Remote Sensing Image Based on UNet Model
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.
building change detectionattention mechanismUNetremote sensing imageencoder
国家科技期刊平台
NSTL
万方数据
维普
