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

• URL: http://arxiv.org/abs/2403.02784v2
• Date: Thu, 24 Oct 2024 13:01:09 GMT
• Title: DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation
• Authors: Lingyan Ran, Lushuang Wang, Tao Zhuo, Yinghui Xing,
• Abstract summary: Unsupervised domain adaptation (UDA) has proven to be advantageous in incorporating unclassified information from the target domain. This paper proposes a hybrid training strategy as well as a novel dual-domain image fusion strategy. The efficacy of our approach is substantiated by extensive benchmark experiments and ablation studies conducted on the ISPRS Vaihingen and Potsdam datasets.
• Score: 6.223876661401282
• License:
• Abstract: 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.

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