Sea ice segmentation on optical images empowered by multi-dimensional attention within a U-Net architecture

Chen Qinze; Qu Jiaming; Bai He; Hao Yuchi

doi:10.12284/hyxb20250115

Volume 47 Issue 12

Dec. 2025

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Article Navigation > Haiyang Xuebao > 2025 > 47(12): 103-113

Chen Qinze，Qu Jiaming，Bai He, et al. Sea ice segmentation on optical images empowered by multi-dimensional attention within a U-Net architecture[J]. Haiyang Xuebao，2025, 47(12)：103–113 doi: 10.12284/hyxb20250115

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Sea ice segmentation on optical images empowered by multi-dimensional attention within a U-Net architecture

doi: 10.12284/hyxb20250115

Chen Qinze^{1, 2
,},
Qu Jiaming^{1, 2
,
,},
Bai He^{1, 2},
Hao Yuchi^{1, 2}

1.
National Engineering Research Center of Dredging Technology and Equipment Co., Ltd., China Communications Construction Company, Shanghai 201208, China
2.
Key Laboratory of Dredging Technology, China Communications Construction Company, Shanghai 201208, China

Received Date: 2025-06-08
Rev Recd Date: 2025-11-17

Available Online: 2025-11-06

Publish Date: 2025-12-31

Abstract

Abstract

Based on Sentinel-2 optical remote sensing imagery, this paper proposes a sea ice segmentation algorithm empowered by multi-dimensional attention within a U-Net architecture. Building upon the classical U-Net, the algorithm innovatively introduces a temporal-aware multi-head attention module at the end of the encoder path. This module enhances spatial perception using learnable spatial positional encodings and utilizes temporal encodings (where the year is processed by min-max normalization, and the month and day are encoded via sine-cosine functions) as query vectors to perform global temporal correlation reasoning on deep image features. Furthermore, a lightweight triple attention module (channel-spatial-temporal) is embedded within the decoder path. This module calculates the weights for these three dimensions and fuses feature information via element-wise multiplication, effectively enhancing key features and focusing on details. To validate the accuracy and effectiveness of the proposed algorithm, classical VIT, DeepLabV3+ and U-Net models were selected as comparative methods, and ablation studies were conducted. Experimental results demonstrate that the proposed algorithm achieves the best performance in terms of OA (Overall Accuracy), Kappa coefficient, and Mean IoU (Intersection over Union) coefficients, reaching 92.11%, 0.846, and 0.574, respectively. The combined effect of the two attention modules enables the model to avoid global bias while improving local classification confidence. Notably, the classification accuracy for 30%−50% ice concentration and fast ice was significantly improved by 48.8% and 31.95%, respectively.
- northeast passage,
- sea ice segmentation,
- multi-dimensional attention,
- U-Net

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References(28)

References

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