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.

翻译：遥感图像的语义分割因存在大量未标注数据而成为一项具有挑战性的热点问题。无监督域适应（UDA）已被证明在整合目标域中的未分类信息方面具有优势。然而，在源域和目标域上独立微调UDA模型对结果的改善效果有限。本文提出了一种混合训练策略以及一种新型双域图像融合策略，该策略有效利用了原始图像、变换图像及中间域信息。此外，为提升伪标签的精确性，我们提出了一种伪标签区域特定权重策略。通过在ISPRS Vaihingen与Potsdam数据集上开展的大量基准实验与消融研究，验证了本文方法的有效性。