Semantic segmentation of high-resolution remote sensing imagery (HRSI) suffers from the domain shift, resulting in poor performance of the model in another unseen domain. Unsupervised domain adaptive (UDA) semantic segmentation aims to adapt the semantic segmentation model trained on the labeled source domain to an unlabeled target domain. However, the existing UDA semantic segmentation models tend to align pixels or features based on statistical information related to labels in source and target domain data, and make predictions accordingly, which leads to uncertainty and fragility of prediction results. In this paper, we propose a causal prototype-inspired contrast adaptation (CPCA) method to explore the invariant causal mechanisms between different HRSIs domains and their semantic labels. It firstly disentangles causal features and bias features from the source and target domain images through a causal feature disentanglement module. Then, a causal prototypical contrast module is used to learn domain invariant causal features. To further de-correlate causal and bias features, a causal intervention module is introduced to intervene on the bias features to generate counterfactual unbiased samples. By forcing the causal features to meet the principles of separability, invariance and intervention, CPCA can simulate the causal factors of source and target domains, and make decisions on the target domain based on the causal features, which can observe improved generalization ability. Extensive experiments under three cross-domain tasks indicate that CPCA is remarkably superior to the state-of-the-art methods.

翻译：高分辨率遥感影像语义分割面临域偏移问题，导致模型在未见过的目标域中性能较差。无监督域自适应语义分割旨在将基于标注源域训练的语义分割模型迁移至未标注目标域。然而，现有无监督域自适应语义分割模型倾向于利用源域与目标域数据中与标签相关的统计信息对齐像素或特征，并据此进行预测，导致预测结果存在不确定性与脆弱性。本文提出一种因果原型启发的对比自适应方法，用于探索不同高分辨率遥感影像域与其语义标签之间的不变因果机制。该方法首先通过因果特征解耦模块从源域与目标域图像中解耦出因果特征与偏置特征，随后利用因果原型对比模块学习域不变因果特征。为进一步消除因果特征与偏置特征的相关性，引入因果干预模块对偏置特征进行干预，生成反事实无偏样本。通过强制因果特征满足可分性、不变性与可干预性原则，因果原型启发的对比自适应方法能够模拟源域与目标域的因果因素，并基于因果特征对目标域做出决策，从而显著提升泛化能力。在三个跨域任务上的大量实验表明，该方法在性能上显著优于现有先进方法。