AED-Net: A High-Resolution Remote Sensing Image Road Extraction Method Integrating Atrous Spatial Pyramid Pooling and Efficient Channel Attention Mechanism

High-resolution remote sensing imagery plays a pivotal role in road extraction due to its abundant semantic information. However, traditional road extraction methods are time-consuming, labor-intensive, and susceptible to subjective factors, leading to inaccurate extraction results. Therefore, this study aims to explore an efficient, accurate, and automated road extraction method. We propose a road extraction model named AED-Net, which employs a lightweight MobileNet v2 as the feature extractor, combines the multi-scale feature extraction capability of ASPP with an encoder-decoder structure, and integrates an ECA mechanism to enhance feature learning ability. During the training process, the BCE-DL loss function is adopted to optimize model performance. Experimental validation on the Massachusetts Roads dataset and Ottawa Road dataset demonstrates that AED-Net outperforms the latest Attention U-Net (AU-Net) and ECA-DeeplabV3+ models. On the Massachusetts Roads dataset, AED-Net achieves an overall accuracy (OA) of 97.60% and a mean intersection over union (MIoU) of 85.29%. Similarly, on the Ottawa Road dataset, AED-Net exhibits superior performance compared to classical semantic segmentation networks such as SegNet, U-Net, and Deeplab V3+, achieving an OA of 98.83% and an MIoU of 88.74%. Furthermore, ablation studies validate the effectiveness and necessity of the proposed improvements. In summary, AED-Net has made significant progress in multi-scale feature extraction, boundary information restoration, and critical feature learning, offering new insights and solutions for the development of road network extraction technologies. Its high accuracy and robustness render AED-Net promising for a wide range of applications in remote sensing image processing, intelligent transportation systems, and beyond.