Abstract
Road extraction from satellite imagery is vital in a broad range of applications. However, extracting complete roads is challenging due to road occlusions caused by the surroundings. This letter proposed an improved encoder–decoder network via extracting road context and integrating full-stage features from satellite imagery, dubbed as RCFSNet. A multiscale context extraction (MSCE) module is designed to enhance inference capabilities by introducing adequate road context. Multiple full-stage feature fusion (FSFF) modules in the skip connection are devised to provide accurate road structure information, and we devise a coordinate dual-attention mechanism (CDAM) to strengthen the representation of road features. Extensive experiments are carried out on two public datasets, and as a result, our RCFSNet outperforms other state-of-the-art methods. The results indicate that the road labels extracted by our method have preferable connectivity. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CVer-Yang/RCFSNet</uri> .
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