A Two-Stage Open Compound Domain Adaptation Framework for Semantic Segmentation in Remote Sensing Imagery

Unsupervised Domain Adaptation (UDA) has emerged as a critical research direction in remote sensing (RS) interpretation, aiming to bridge the gap between the labeled source and unlabeled target domains. However, most methods are designed for either a single target domain or multi-domain setting with clear boundaries, which necessitates retraining UDA models for each target domain, making it even harder to directly generalize to unseen domains. This paper proposes a novel two-stage open compound domain adaptation framework for semantic segmentation in RS images, which models the target domain as a composite of multiple unknown but homogeneous domains and leverages image translation and meta-learning techniques. In the first stage, we meticulously design a cross-domain image translation model (CDIT) based on contrastive learning to rapidly align the appearance of target domain images with the source domain style. In the second stage, the translated target images are first processed by a pre-trained model to generate pseudo-labels. Subsequently, a dynamic class-wise memory model (DCWM) is designed to progressively update abstract categorical features, serving as external class guidance for semantic segmentation within a meta-learning framework. Specifically, meta-training is employed to iteratively learn and update domain-agnostic categorical memory of semantic classes, while meta-testing simulates memory retrieval and gradually refines the categorical memory using pseudo-labels to adapt to new domains. Additionally, intra-class cohesion and inter-class divergence losses are incorporated to enhance the abstraction and retention of categorical features, aligning more closely with human cognitive patterns. Extensive experiments on RS benchmarks and unseen real images demonstrate the superior generalization of our method compared to state-of-the-art approaches.