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

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.

Accepted to IEEE Transactions on Geoscience and Remote Sensing

References in corpus (7)

• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
• UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
• SpectralGPT: Spectral Remote Sensing Foundation Model
• Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data
• Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images
• Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory

Cited by in corpus (2)

• Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
• AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluation