Few-Shot Domain Adaptation Transformer for Hyperspectral Image Change Detection

Deep learning has shown strong performance in hyperspectral images change detection (HSIs-CD), but it typically requires a large amount of labeled data. Acquiring such labeled data is often expensive, time-consuming, and infeasible in many application scenarios. Few-shot learning (FSL) addresses this limitation by training a model on a large number of training tasks and then applying it to test tasks with only a few labeled samples. However, in HSIs-CD, variations in imaging conditions lead to significant distribution gaps across datasets. Consequently, FSL models trained on the training tasks typically generalize poorly to test tasks and may fail to extract meaningful features. To address this, we propose FSDA-Former, a few-shot domain adaptation transformer for HSIs-CD. First, it employs a generative pretraining strategy based on a masked autoencoder (FSDA-MAE) to learn domain-agnostic representations and improve feature extraction across diverse tasks. Second, an FSDA Transformer layer (FSDA-TL) is introduced to refine the extracted features in a meta-learning manner, thereby learning class-discriminative representations and constructing a task-specific metric space. Third, a two-stage domain adaptation strategy (FSDA-TDA) is developed to further adapt FSDA-Former to the feature domain of the test task. FSDA-TDA further enhances the generalization ability of FSDA-Former under distribution shifts between training and test tasks. Experimental results on three real-world HSIs-CD datasets demonstrate that FSDA-Former consistently outperforms existing few-shot baselines. It achieves up to 3.60% improvement in Kappa and 1.58% in overall accuracy. These results demonstrate the effectiveness and superiority of FSDA-Former in HSIs-CD, even when only a few labeled examples are available.