  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.
