To solve the UNet3+network with depth deepening a large number of fusion redundant operation that the model training time is too long and resulting in road extraction using less problems,the UNet3+network improve-ment,by cutting UNet3+network hierarchy using Bottleneck module to replace the convolution layer in the original network,retain the network feature fusion ability and reduce the network complexity,and introduce hybrid attention mechanism optimization model,build a new network model.The improvement method is compared with several typical road extraction models.The experimental results show that:(1)compared with Unet3+network,the proposed method improves by 4.72％,2.46％,4.84％and 2.01％respectively,all better than the comparison algorithm;(2)com-pared with several classical feature extraction models,the improved model has better recognition effect,and phenoty-ping in the accuracy,connectivity,integrity and other aspects of road extraction.
deep learningattention mechanismUNet3+path extractionskip connection
NETL
NSTL
万方数据
