mlsanityData to 7 Oct 2026

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

arXiv 2403.02784 ↗First posted 5 Mar 2024cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 48 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

29 of 48 references.

References · 29

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17 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Taskscited by 3
  2. 2024AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluationcited by 2
  3. 2025Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Surveycited by 2
  4. 2025Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentationcited by 1