Unsupervised Remote Sensing Image Semantic Segmentation Based on Multiscale Contrastive Domain Adaptation

Unsupervised domain adaptation (UDA) for remote sensing image semantic segmentation aims to train a deep model on the labeled source domain and apply it to the unlabeled target domain. However, resolution and scene inconsistencies of cross-domain remote sensing images lead to great distribution differences, which weakens the semantic segmentation effect. To solve the above issues, an unsupervised remote sensing image semantic segmentation method is proposed based on multiscale contrastive domain adaptation. First, the mean teacher model is introduced into the UDA paradigm to generate pseudo-labels for target-domain data, thereby achieving the cross-domain segmentation capability. A dynamic class balance sampling (DCBS) method is proposed to mitigate the class imbalance problem in cross-domain data by increasing the sampling frequency of the categories with fewer samples. Then, a data augmentation method called cross-domain mixup (CDMix) is developed to reduce the gap between the source and target domains. Finally, a multiscale cross-domain contrastive loss (MCCL) is developed, which introduces contrastive learning to learn domain-consistent features across the source and target domains, resulting in a more coherent and discriminative feature representation. Experimental results show that the proposed method can yield superior performance for unsupervised remote sensing image semantic segmentation.