研究雷达图像水体分割中人工标注错误的鲁棒性，发现U-Net可容忍一定噪声。
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images
- 用对抗攻击模拟人工标注错误，测试U-Net对标签噪声的敏感度。
- 实验表明模型在标注错误率15%以下时性能仍稳定，超过后显著下降。
- 适合关注遥感图像标注质量与模型鲁棒性的研究人员参考。
从合成孔径雷达（SAR）图像中分割内陆水体是洪水监测等应用的重要任务。尽管SAR可在全天气条件下获取高分辨率图像，但区分水体与类水表面仍具挑战，尤其因大河盆地等复杂几何结构。目前广泛采用的U-Net模型常依赖人工标注生成水体掩码作为真实标签，而人工标注易受数据投毒攻击影响。本文通过对抗攻击模拟人工标注误差，研究U-Net对标注噪声的鲁棒性。结果表明，当标注错误率低于15%时，模型性能保持稳定；超过该阈值后性能显著下降。该发现凸显了标注质量对分割效果的关键作用。相关代码、新数据集及对抗样本已公开（GitHub链接：https://github.com/GVCL/IWSeg-SAR-Poison.git）。
原文摘要 · Abstract (English)
Inland water body segmentation from Synthetic Aperture Radar (SAR) images is an important task needed for several applications, such as flood mapping. While SAR sensors capture data in all-weather conditions as high-resolution images, differentiating water and water-like surfaces from SAR images is not straightforward. Inland water bodies, such as large river basins, have complex geometry, which adds to the challenge of segmentation. U-Net is a widely used deep learning model for land-water segmentation of SAR images. In practice, manual annotation is often used to generate the corresponding water masks as ground truth. Manual annotation of the images is prone to label noise owing to data poisoning attacks, especially due to complex geometry. In this work, we simulate manual errors in the form of adversarial attacks on the U-Net model and study the robustness of the model to human errors in annotation. Our results indicate that U-Net can tolerate a certain level of corruption before its performance drops significantly. This finding highlights the crucial role that the quality of manual annotations plays in determining the effectiveness of the segmentation model. The code and the new dataset, along with adversarial examples for robust training, are publicly available. (GitHub link - https://github.com/GVCL/IWSeg-SAR-Poison.git)
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