Road extraction algorithm for remote sensing images based on improved U-Net
In response to the problems of significant omissions,errors,and low extraction accu-racy in road extraction from remote sensing images under different land cover backgrounds,this paper constructs a Res50CBAM Net model based on the U-Net model.Firstly,the model re-places the feature extraction network of the original U-Net model with ResNet50,deepening the depth of the feature extraction network and improving its feature extraction capability;Sec-ondly,the addition of convolutional block attention mechanism in the U-Net skip connection layer enhances the model's ability to recognize road targets.The results show that the model constructed in this article has better extraction performance in different scenarios,with an inter-section to union ratio and F1 score improvement of 2.72％and 2.26％,respectively,compared to the original model.
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