TransLinkNet: LinkNet with transformer for road extraction

Chunle Miao; Chang'an Liu; Zheng Zhang; Qing Tian

doi:10.1117/12.2634524

12 May 2022 TransLinkNet: LinkNet with transformer for road extraction

Chunle Miao, Chang'an Liu, Zheng Zhang, Qing Tian

Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 121730O (2022) https://doi.org/10.1117/12.2634524
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China

Abstract

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.

Citation Download Citation

Chunle Miao, Chang'an Liu, Zheng Zhang, and Qing Tian "TransLinkNet: LinkNet with transformer for road extraction", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 121730O (12 May 2022); https://doi.org/10.1117/12.2634524

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