  Semantic segmentation of remote sensing images is a challenging and hot issue
due to the large amount of unlabeled data. Unsupervised domain adaptation (UDA)
has proven to be advantageous in incorporating unclassified information from
the target domain. However, independently fine-tuning UDA models on the source
and target domains has a limited effect on the outcome. This paper proposes a
hybrid training strategy as well as a novel dual-domain image fusion strategy
that effectively utilizes the original image, transformation image, and
intermediate domain information. Moreover, to enhance the precision of
pseudo-labels, we present a pseudo-label region-specific weight strategy. The
efficacy of our approach is substantiated by extensive benchmark experiments
and ablation studies conducted on the ISPRS Vaihingen and Potsdam datasets.
