用颜色空间优化卫星图像海岸线分割，提升精度与稳定性。
Multi-Modal Robust Enhancement for Coastal Water Segmentation: A Systematic HSV-Guided Framework
- 引入HSV颜色空间监督，增强分割对复杂光谱的适应性。
- 完整框架使训练方差降低84%，边界分割更准确。
- 适合遥感、海洋监测等需要高鲁棒性的应用研究者。
从卫星影像中分割海岸水体面临光谱特性复杂和边界不规则的挑战。传统基于RGB的方法在多样海况下易出现训练不稳定和泛化能力差的问题。本文提出系统性鲁棒增强框架Robust U-Net，结合HSV颜色空间监督与多模态约束，实现更优的海岸水体分割。该方法集成五项协同组件：HSV引导的颜色监督、基于梯度的海岸线优化、形态学后处理、海区清理及连通性控制。消融实验表明，HSV监督影响度最高（0.85），完整框架显著提升训练稳定性（方差降低84%），并全面改善分割质量。方法在多个评估指标上表现一致提升，同时保持计算效率。为保证可复现性，训练配置与代码已开源：https://github.com/UofgCoastline/ICASSP-2026-Robust-Unet。
原文摘要 · Abstract (English)
Coastal water segmentation from satellite imagery presents unique challenges due to complex spectral characteristics and irregular boundary patterns. Traditional RGB-based approaches often suffer from training instability and poor generalization in diverse maritime environments. This paper introduces a systematic robust enhancement framework, referred to as Robust U-Net, that leverages HSV color space supervision and multi-modal constraints for improved coastal water segmentation. Our approach integrates five synergistic components: HSV-guided color supervision, gradient-based coastline optimization, morphological post-processing, sea area cleanup, and connectivity control. Through comprehensive ablation studies, we demonstrate that HSV supervision provides the highest impact (0.85 influence score), while the complete framework achieves superior training stability (84\% variance reduction) and enhanced segmentation quality. Our method shows consistent improvements across multiple evaluation metrics while maintaining computational efficiency. For reproducibility, our training configurations and code are available here: https://github.com/UofgCoastline/ICASSP-2026-Robust-Unet.
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