A Context-Aware Road Extraction Method for Remote Sensing Imagery Based on Transformer Network

In remote sensing images, roads are usually in complex shapes and can be partially occluded by buildings, trees, and other surroundings. To extract a complete and continuous road network is still a challenging job. This paper proposes a context-aware road extraction method for remote sensing imagery based on Transformer network, in which, a foreground feature enhancement module (FFEM) is designed to further extract detailed road features such as contours from the shallowest feature map; Dual-attention module (DAM) is constructed and applied at different skip connections to make the model focus more on road features in the different level of feature maps; A Swin Transformer-based contextual information extraction module (CIEM) is built between the encoder and decoder modules to capture the global and local road contextual information so as to recover the occluded roads information as much as possible. Furthermore, a multi-scale decoder (M-Decoder) is designed to improve the feature map recovery ability of the decoder module. Experiments on the DeepGlobe road dataset are conducted to verify the efficiency of the proposed method.