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.

该研究论文提出了一种混合训练策略和新颖的双域图像融合策略，有效利用原始图像、变换图像和中间域信息，并提出了一种伪标签区域特定权重策略，以增强伪标签的精确性。ISPRS Vaihingen 和 Potsdam 数据集上进行的广泛基准实验和消融研究证实了我们方法的有效性。