用原型引导伪标签去噪，提升遥感图像语义分割的无源域适应效果
Prototype-Based Pseudo-Label Denoising for Source-Free Domain Adaptation in Remote Sensing Semantic Segmentation
- 用原型加权伪标签，减少目标域噪声影响
- 在多个遥感数据集上性能超越现有方法，提升显著
- 适合遥感图像分割、无监督域适应研究者参考
无源域适应（SFDA）可在仅使用预训练源模型和未标注目标域数据的情况下，实现遥感图像（RSI）的语义分割。然而，目标域缺乏真实标签常导致生成噪声伪标签，干扰域偏移（DS）的有效缓解。为此，我们提出ProSFDA框架，采用原型加权伪标签，促进在伪标签噪声下的可靠自训练（ST）。此外，引入原型对比策略，增强同类别特征聚集，使模型在无需真实标签监督下学习判别性目标域表征。大量实验表明，该方法显著优于现有方法。
原文摘要 · Abstract (English)
Source-Free Domain Adaptation (SFDA) enables domain adaptation for semantic segmentation of Remote Sensing Images (RSIs) using only a well-trained source model and unlabeled target domain data. However, the lack of ground-truth labels in the target domain often leads to the generation of noisy pseudo-labels. Such noise impedes the effective mitigation of domain shift (DS). To address this challenge, we propose ProSFDA, a prototype-guided SFDA framework. It employs prototype-weighted pseudo-labels to facilitate reliable self-training (ST) under pseudo-labels noise. We, in addition, introduce a prototype-contrast strategy that encourages the aggregation of features belonging to the same class, enabling the model to learn discriminative target domain representations without relying on ground-truth supervision. Extensive experiments show that our approach substantially outperforms existing methods.
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