Work

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

Year: 2025

Type: article

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.

Source: Remote Sensing

DOI: 10.3390/rs17233839

Authors: Jianxin He, Shuo Yang, Haoyu Wang, Wanshou Liu, Xiong Deng

Institutions: Harbin Engineering University, Naval University of Engineering

Language: English

Topic: Arctic and Antarctic ice dynamics

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $3,258