Probabilistic Multiple Prototypes Contrastive Alignment for Cross-Domain Semantic Segmentation of Remote Sensing Images (opens in new tab)

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain to improve model generalization, and has been widely used in remote sensing image (RSI) semantic segmentation. However, complex intradomain category distributions and mismatched cross-domain category priors hinder accurate domain alignment in existing UDA methods. To address these challenges, we propose a probabilistic multiple prototypes contrastive alignment (PMPCA) UDA...