RBFNet: A Region-Aware and Boundary-Enhanced Fusion Network for Road Extraction From High-Resolution Remote Sensing Data

Most existing road extraction methods prioritize region accuracy at the expense of ignoring road boundaries and connectivity quality. The occluding objects such as buildings, trees, and vehicles in remote sensing data usually cause discontinuous mask outputs, and consequently affect road extraction accuracy. In this article, a road extraction fusion network perceiving region and boundary features is proposed. The combination of a location-aware transformer and convolutional neural network is responsible for focusing regional semantic information through adaptive weight filtering. Combining spatial and channel information, the graph convolutional network is improved by constructing an integrated adjacency matrix to consider the relationships between nodes at different scales, which allows for better capture of multiscale contexts. Boundary details are used to complement regional features, thereby enhancing the connectivity of masks. Comprehensive quantitative and qualitative experiments demonstrate that our method significantly outperforms state-of-the-art methods on two public benchmarks, which can improve road extraction by handling interruptions related to shadows and occlusions, producing high-resolution masks.