Inter-Sensor High-Resolution and Multi-Temporal Image Fusion for Unsupervised Domain Adaptation in Remote Sensing

Motivated by the increasing demand for robust segmentation in unlabeled remote sensing data, we propose domain adaptation multimodal and multi-temporal transformer (DAM-Former), a novel unsupervised domain adaptation (UDA) model that fuses high-resolution (HR) multimodal imagery with multi-temporal multispectral data. Current UDA approaches in remote sensing rarely exploit the complementary strengths of spatial and temporal features. To address this gap, our framework integrates two interconnected branches: a transformer-based network for HR multimodal data and a lightweight convolutional network with temporal attention for multi-temporal imagery. To improve segmentation accuracy and lower noise, the extracted features are robustly combined through a deep temporal fusion (DTF) module and a new mixed loss (ML) with an ensemble pseudo-label (EP) strategy. Extensive experiments and an ablation study on the FLAIR-2 dataset demonstrate that DAM-Former outperforms state-of-the-art methods, marking the first in-depth study of temporal information fusion in UDA segmentation for remote sensing data. Code available at https://github.com/ibanezfd/DAM_Former.