GDformer: a lightweight decoder for efficient semantic segmentation of remote sensing urban scene imagery

Bing Liu; Zhaohao Zhong

doi:10.1117/12.3021480

19 February 2024 GDformer: a lightweight decoder for efficient semantic segmentation of remote sensing urban scene imagery

Bing Liu, Zhaohao Zhong

Author Affiliations +

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130630M (2024) https://doi.org/10.1117/12.3021480
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

Semantic segmentation of remote sensing urban scene imagery is a dense prediction task, which has been applied to the land-cover or land-use category. However, the dimension of remote sensing image is huge, which will result in the huge computation cost. In order to reduce the computation cost, a common method is to design a lightweight decoder to achieve a good trade-off between accuracy and computation cost. For this purpose, we design a lightweight transformer-based decoder GDformer. The GDformer consists of our proposed Global Value Transformer and Dynamic feature fusion module. The Global Value Transformer can extract the global semantic feature and the Dynamic feature fusion module can fuse the local feature and global semantic feature dynamic to capture the local-global context with a good trade-off, and the local-global context has been proved is necessary for the semantic segmentation of remote sensing. Extensive experiments prove that our proposed method can achieve a good trade-off between and state-of-the-art performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bing Liu and Zhaohao Zhong "GDformer: a lightweight decoder for efficient semantic segmentation of remote sensing urban scene imagery", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130630M (19 February 2024); https://doi.org/10.1117/12.3021480

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