Research on building extraction based on CBAM VGG16-UNet semantic segmentation model
Aiming at the problems of"missed detection","false detection"and"holes"in remote sensing image building extraction,this study proposes a CBAM VGG16-UNet network with dual attention mechanism for building extraction research.In the downsampling part,the first five convolution blocks of VGG16 are used to replace the encoder part of the U-Net network.The dual attention mechanism CBAM is introduced in the fusion of each feature of the upsampling,and the transposition convolution of U-Net is replaced by billinear interpolation.In this study,the WHU building dataset and Guiyang building dataset are used to verify the model,and compared with three building extraction network models such as Mobile-UNet,U-Net and VGG16-UNet.Experiments show that the precision,recall rate,F1-score and IoU of CBAM VGG16-UNet on WHU building dataset reach 94.90％,95.46％,95.18％and 90.80％.On the Guiyang building dataset,the accuracy,recall.F1-score and IoU reach 77.53％,84.46％,80.85％and 67.85％,which are better than the three comparison models.This study provides a new idea for solving common problems in building extraction,which has certain engineering application value.
U-NetVGG16CBAMbuilding extractionWHU building data set
NETL
NSTL
万方数据
