Research on Road Extraction Method from Remote Sensing Image Based on Improved U-Net Model
In view of high-resolution remote sensing images being with complex background information,road extraction in the high-resolution remote sensing images being difficult and having a low degree of automation,this paper proposes an improved U-Net road extraction method.First,the VGG16 network structure is employed in the encoder to replace the original U-Net encoder structure,then,a feature compression activation module(SENet)is added after each encoder and decoder block to enhances the ability of network feature learning.Finally,the loss function combined with the Dice loss function and the binary cross-entropy loss function is used for training,which reduces the sample imbalance problem in the road extraction task.The experimental results on the Massachusetts Road data set show that the improved algorithm has effectively improved the road extraction results.The preci-sion,recall,F1-score and mIoU evaluation indicators of the proposed method on the test set reached 82.5%,77.8%,80.0%,and 82.1%,respectively.In the test image,it has a better recognition effect on roads with different widths and irregular shapes.
U-Netremote sensing imagesroad extractionSENetloss function
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