用形态扰动生成合成标签，提升雷达油污分割跨区域泛化能力
Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation
- 通过几何形态扰动生成更真实的油污与相似区域
- 在秘鲁海域提升平均交并比6个百分点，小目标提升超10%
- 适合数据稀缺地区遥感油污检测模型训练
合成雷达油污分割数据的深度学习模型常因海况、后向散射统计及油膜形态差异，在不同区域间泛化能力差，尤其在缺乏标注数据的秘鲁海岸更为严重。为此，我们提出两阶段合成增强框架 MORP--Synth，以改善从地中海到秘鲁的迁移效果。第一阶段采用形态区域扰动（Morphological Region Perturbation），基于曲率引导标签空间生成真实几何变化；第二阶段使用条件生成式 INADE 模型，从修正掩码中渲染类 SAR 纹理。我们构建了包含2112个512×512标注块的秘鲁数据集，涵盖40景哨兵-1影像（2014–2024年），并与地中海 CleanSeaNet 基准对齐，评估七种分割架构。在秘鲁域上，基于地中海预训练模型的mIoU从67.8%降至51.8%；而使用 MORP--Synth 后性能最高提升+6 mIoU，少数类指标显著改善（油污+10.8，相似物+14.6）。
原文摘要 · Abstract (English)
Deep learning models for SAR oil spill segmentation often fail to generalize across regions due to differences in sea-state, backscatter statistics, and slick morphology, a limitation that is particularly severe along the Peruvian coast where labeled Sentinel-1 data remain scarce. To address this problem, we propose \textbf{MORP--Synth}, a two-stage synthetic augmentation framework designed to improve transfer from Mediterranean to Peruvian conditions. Stage~A applies Morphological Region Perturbation, a curvature guided label space method that generates realistic geometric variations of oil and look-alike regions. Stage~B renders SAR-like textures from the edited masks using a conditional generative INADE model. We compile a Peruvian dataset of 2112 labeled 512$\times$512 patches from 40 Sentinel-1 scenes (2014--2024), harmonized with the Mediterranean CleanSeaNet benchmark, and evaluate seven segmentation architectures. Models pretrained on Mediterranean data degrade from 67.8\% to 51.8\% mIoU on the Peruvian domain; MORP--Synth improves performance up to +6 mIoU and boosts minority-class IoU (+10.8 oil, +14.6 look-alike).
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