Road extraction from remote sensing images based on attention and adaptive feature fusion

To tackle the challenges of inadequate feature representation, detail degradation, and imbalanced class distribution in road extraction from remote sensing imagery, this research introduces a road extraction model from remote sensing images based on attention and adaptive feature fusion Unet (AAF-Unet). The model uses VGG16 as the backbone network. Firstly, the cross-spatial efficient multi-scale attention (CEMA) module is added at the skip connection of the Unet network, effectively integrating global contextual information and enhancing the model's feature perception ability for complex backgrounds and occluded areas. Secondly, add the adaptive convolution mix (ACmix) module at the bridge between the encoder and the decoder. By combining dynamic convolution with self-attention mechanisms, this module enables efficient integration of local detail features and global context information, significantly improving the model's performance in edge and detail recovery. In the loss function section a composite loss function combining Dice loss and Focal loss is proposed, optimizing the model's performance in segmentation tasks involving small targets and edge-blurred regions. Lastly, through the experimental verification on different dataset, the mIoU index of AAF-Unet model reached 82.01% on DeepGlobe dataset and 79.36% on the CHN6-CUG dataset. Compared with Unet, Deeplabv3+ and Hrnet models, the extraction results were increased respectively on two dataset. Good segmentation accuracy was achieved while ensuring the generalization ability of the model.