Semantic segmentation of high spatial resolution remote sensing imagery based on weighted attention U-Net

Yue Zhang; Leiguang Wang; Ruiqi Yang; Nan Chen; Yili Zhao; Qinling Dai

doi:10.1117/12.2680206

27 June 2023 Semantic segmentation of high spatial resolution remote sensing imagery based on weighted attention U-Net

Yue Zhang, Leiguang Wang, Ruiqi Yang, Nan Chen, Yili Zhao, Qinling Dai

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051H (2023) https://doi.org/10.1117/12.2680206
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

In recent years, with the development of deep learning and attention mechanism, more research has been carried out to realize semantic image segmentation based on deep learning integrated attention mechanisms. However, the current semantic segmentation methods have low segmentation accuracy, high computation cost, and serious loss of detailed information. In this paper, a lightweight designed attention gate model was introduced to reduce the computation cost. And because it can suppress irrelevant regions in the input image, while highlighting the salient features of specific tasks, the combination of the two weighting factors input features ( 𝑥^𝑙 ) and gating signal (g) in this structure can improve segmentation accuracy and reduce loss of detail. Therefore, this study used the weighted attention U-Net network to perform semantic segmentation on the GID dataset and finally evaluated it on the four indicators of Precision, Recall, F1-Sorce, and mIoU. This result shows that different weight values have a more significant impact on the experimental results. The attention U-Net with the best weight combination compared with the traditional U-Net network, Precision, Recall, F1-Sorce, and mIoU are increased by 0.88%, 1.4%, 1.13%, and 1.2%, respectively. Compared with the original attention UNet, Precision, Recall, F1-Sorce, and mIoU are increased by 0.86%, 1.24%, 1.04%, and 1.75%, respectively.

Citation Download Citation

Yue Zhang, Leiguang Wang, Ruiqi Yang, Nan Chen, Yili Zhao, and Qinling Dai "Semantic segmentation of high spatial resolution remote sensing imagery based on weighted attention U-Net", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051H (27 June 2023); https://doi.org/10.1117/12.2680206

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KEYWORDS

Image segmentation

Semantics

Network architectures

Remote sensing

Deep learning

Grazing incidence

Spatial resolution

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