通过标签特定变形增强，提升遥感图像分割的鲁棒性
NSegment : Label-specific Deformations for Remote Sensing Image Segmentation
- 仅对分割标签施加弹性变形，按样本动态调整强度
- 在多个主流模型上实现性能提升，有效缓解标注噪声
- 方法简单高效，无需复杂训练流程，适合数据稀缺场景
遥感图像分割数据集中标注错误常因类别边界模糊、混合像素、阴影及地形复杂等因素隐含存在，且标注成本高导致数据稀缺，难以训练抗噪声模型。现有标签筛选或噪声修正方法虽有效，但增加训练时间与实现复杂度。本文提出NSegment——一种简单高效的增强策略，仅对分割标签施加弹性变形，并在每个训练周期内按样本动态调整变形强度，以应对标注不一致问题。实验表明，该方法显著提升多种先进模型在遥感图像分割任务上的性能。
原文摘要 · Abstract (English)
Labeling errors in remote sensing (RS) image segmentation datasets often remain implicit and subtle due to ambiguous class boundaries, mixed pixels, shadows, complex terrain features, and subjective annotator bias. Furthermore, the scarcity of annotated RS data due to the high cost of labeling complicates training noise-robust models. While sophisticated mechanisms such as label selection or noise correction might address the issue mentioned above, they tend to increase training time and add implementation complexity. In this paper, we propose NSegment-a simple yet effective data augmentation solution to mitigate this issue. Unlike traditional methods, it applies elastic transformations only to segmentation labels, varying deformation intensity per sample in each training epoch to address annotation inconsistencies. Experimental results demonstrate that our approach improves the performance of RS image segmentation over various state-of-the-art models.
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