发表机构
University of Turku; Finnish Geospatial Research Institute, National Land Survey of Finland（图尔库大学; 芬兰国家土地测量局芬兰地理空间研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高分辨率多光谱影像弱监督水体分割中伪标签噪声问题，提出两阶段框架，利用提示引导局部细化，在多个模型上提升IoU和F1，改善边界与细结构。
AI 中文摘要
高分辨率水体制图支持环境监测及相关应用，但准确的像素级标签难以且成本高昂地生成。官方水文矢量数据提供了可扩展的弱监督，但它们包含边界噪声、时间不匹配以及小型水体结构遗漏等伪影。我们提出了一种用于高分辨率多光谱影像中弱监督水体分割的两阶段框架。第一阶段从栅格化的矢量伪标签学习初始掩膜，第二阶段将这些掩膜转换为结构化的组件级提示，用于局部细化。在人工校正的验证集上，细化将SegFormer-B0的IoU从0.9509提升至0.9535，将U-Net的IoU从0.9408提升至0.9486，相应的F1分数分别从0.9749提升至0.9762和从0.9695提升至0.9736。它带来了更锐利的海岸线、减少的边界溢出以及更好的细长结构描绘。结果表明，提示引导的细化可以通过针对全局训练监督难以捕捉的局部误差来改善基于伪标签的水体分割。
英文摘要
High-resolution water mapping supports environmental monitoring and related applications, but accurate pixel-level labels are difficult and costly to produce. Official hydrographic vectors provide scalable weak supervision, but they contain artifacts like boundary noise, temporal mismatch, and omissions of small water structures. We propose a two-stage framework for weakly supervised water segmentation in high resolution multispectral imagery. Stage 1 learns initial masks from rasterized vector pseudo-labels, and Stage 2 converts these masks into structured component-wise prompts for localized refinement. On a manually corrected validation set, refinement improves SegFormer-B0 from 0.9509 to 0.9535 IoU and U-Net from 0.9408 to 0.9486 IoU, with corresponding F1 gains from 0.9749 to 0.9762 and 0.9695 to 0.9736. It leads to sharper shorelines, reduced boundary spillover, and better thin-structure delineation. The results indicate that prompt-guided refinement can improve pseudo-label-based water segmentation by targeting local errors that are poorly captured by global training supervision.
CommentsAccepted for presentation at ICIP Workshop 2026 and to be published as part of the conference proceedings
