An enhanced global context-aware network for road extraction from high-resolution imagery

Abstract

ABSTRACT Currently, the automation of road segmentation in high-resolution satellite imagery is inadequate, and a part of the road network information is affected by the loss of spatial features and lack of global context information. To overcome this, we propose a deep convolutional neural network called the enhanced global context-aware network (EGC-Net). The EGC-Net employs a newly enhanced global context-aware module, which adds convolution kernels of different scales based on atrous spatial pyramid pooling (ASPP). In addition, the feature aggregation and self-attention modules further improve the accuracy of road segmentation. Our experimental results show that the performance of EGC-Net is superior to that of state-of-the-art methods. When tested using the Massachusetts Roads Dataset, the EGC-Net achieved an F1-score (evaluation metric for the harmonic mean of the precision and recall metrics) of 0.8234, accuracy of 0.9466, and the intersection of the prediction and ground truth regions over their union (IoU) of 0.8195. Overall, EGC-Net performs better than other deep learning algorithms under different evaluation indexes. The ablation study indicates that the IoU score of our enhanced global context-aware module is higher than that of ASPP by 0.0207, and each component is required to obtain the best road detection results.