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Remote Sensing
Volume 17
Issue 23
10.3390/rs17233839
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach

by
Jianxin He
1,
Shuo Yang
2,
Haoyu Wang
1,
Wanshou Liu
3 and
Xiong Deng
1,*
1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
2
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
3
Qingdao Hatran Ocean Intelligence Technology Co., Ltd., Qingdao 266000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3839; https://doi.org/10.3390/rs17233839 (registering DOI)
Submission received: 13 October 2025 / Revised: 19 November 2025 / Accepted: 26 November 2025 / Published: 27 November 2025
(This article belongs to the Special Issue AI Technology for Enhanced Analysis of High-Resolution Earth Observation Imagery in Climate Change)
Versions Notes

Abstract

Fine-scale detailed estimation of sea ice concentration (SIC) is pivotal for maritime safety, scientific exploration, and environmental surveillance. However, current datasets frequently present challenges due to their limited resolution, thereby hindering fine-scale analysis of sea ice conditions. This paper introduces a novel Deep Self-Attention Residual U-Net (Deep SARU-Net) architecture to address the limitations inherent in existing super-resolution estimation techniques. By harnessing distinctive multi-stage self-attention mechanisms, orthogonal rectangular convolutional kernels, and residual modules, this architecture significantly augments both the precision and generalizability of SIC super-resolution estimation tasks. Experimental results demonstrate that in the vicinity of the Chukchi Sea, the Deep SARU-Net method exhibits superior performance in terms of both RMSE and SSIM values compared to other models, showcasing its efficacy. Furthermore, generalization analyses across diverse sea regions confirm the model’s universality.
Keywords: sea ice; super-resolution; deep learning; self-attention mechanism sea ice; super-resolution; deep learning; self-attention mechanism

Share and Cite

MDPI and ACS Style

He, J.; Yang, S.; Wang, H.; Liu, W.; Deng, X. Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach. Remote Sens. 2025, 17, 3839. https://doi.org/10.3390/rs17233839

AMA Style

He J, Yang S, Wang H, Liu W, Deng X. Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach. Remote Sensing. 2025; 17(23):3839. https://doi.org/10.3390/rs17233839

Chicago/Turabian Style

He, Jianxin, Shuo Yang, Haoyu Wang, Wanshou Liu, and Xiong Deng. 2025. "Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach" Remote Sensing 17, no. 23: 3839. https://doi.org/10.3390/rs17233839

APA Style

He, J., Yang, S., Wang, H., Liu, W., & Deng, X. (2025). Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach. Remote Sensing, 17(23), 3839. https://doi.org/10.3390/rs17233839

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