DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation With Unsupervised Domain Adaptation

The semantic segmentation of remote sensing (RS) images is a challenging and hot issue due to the large amount of unlabeled data and domain variation. Unsupervised domain adaptation (UDA) has proven to be advantageous in leveraging unlabeled information from the target domain. However, traditional approaches of independently fine-tuning UDA models in the source and target domains have a limited effect on the result. In this article, we propose a hybrid training strategy that boosts self-training methods with domain fusion images. First, we introduce a novel dual-domain image fusion (DDF) strategy to effectively utilize the original image, the style-transferred image, and the intermediate-domain information. Second, to further refine the precision of pseudolabels, we present a region-specific reweighting strategy that assigns different weights to pseudolabel regions based on their spatial context. Finally, we conduct a series of extensive benchmark experiments and ablation studies on the ISPRS Vaihingen and Potsdam datasets. These results show the efficiency of our approach and establish a practical basis for implementing semantic segmentation in remote sensors.