TransLinkNet: LinkNet with transformer for road extraction

Road extraction from remote sensing image is a fundamental task. Although, the methods based on CNNs have achieved great progress. It is difficult for network-based on CNNs to achieve a breakthrough in performance due to the limitation of receptive field. However, Transformer has better capabilities to build the global receptive field than CNNs. This paper proposes a novel network called TransLinkNet which combines CNNs and Transformer to obtain robust feature representations. Specifically, a stack of Transformer blocks is interspersed between LinkNet layers. Convolution operations are good at obtaining local features, while the attention mechanism in Transformer to build the global receptive field. Experiments have proved that the model achieves competitive performance on The Massachusetts dataset.