SFG-UNet: a state-space-guided U-Net for glacier segmentation in optical remote sensing imagery

Abstract

Glacier segmentation in optical remote sensing imagery remains challenging in complex mountain environments due to fragmented glacier structures, blurred boundaries, seasonal snow confusion, terrain shadows, bare rock, and cloud interference. To address these issues, this study proposes a state-space-guided U-Net framework, termed SFG-UNet, for glacier segmentation in optical remote sensing imagery. The model introduces a long-range state space block in the encoder to enhance global contextual representation, an SSM-guided frequency decoupling and boundary calibration module in the skip pathway to refine low- and high-frequency features, and a semantic-guided full-scale gated fusion module in the decoder to improve selective multi-scale feature aggregation. Experiments were conducted on a self-built Landsat-8/9 glacier dataset from the Animaqing Snow Mountain region and an independent public DL4GAM Alps dataset for external validation. On the Animaqing dataset, SFG-UNet achieved 95.71% accuracy, 95.04% dice, 90.08% kappa, and 90.61% MIoU, outperforming representative CNN-based, attention-based, Transformer-based, frequency-domain, glacier-oriented, and SSM-based segmentation methods. On the external DL4GAM Alps dataset, SFG-UNet also achieved the best overall performance, with 89.86% accuracy, 89.12% dice, 81.02% kappa, and 83.64% MIoU. Seasonal, scenario-based, complexity, ablation, and residual error analyses further demonstrate that SFG-UNet improves glacier continuity, boundary recovery, and robustness under complex optical imaging conditions while maintaining an acceptable computational cost.