Two-Stage Domain Adaptation for Hyperspectral Image Classification Based on Self-Distillation and Test-Time Adaptation

Unsupervised domain adaptation methods can effectively mitigate the spectral drift in cross-scene hyperspectral image classification. Among them, adversarial training methods are particularly noteworthy due to their outstanding performance. However, due to the inherent mechanism of adversarial training, these methods suffer from continuing training instability and limited classifier generalizability. To overcome these limitations, this letter proposes a two-stage domain adaptation (TSDA) framework that incorporates self-distillation and test-time adaptation. The self-distillation strategy promotes stability during adversarial training by improving training consistency across iterations. Specifically, each batch includes a subset of data from the previous iteration, and self-distillation ensures that the output of this subset in the current iteration is consistent with the previous one. This mechanism stabilizes gradient computations during the training process, facilitating more robust parameter updates. Subsequently, the test-time adaptation module utilizes a limited set of unlabeled target domain samples to refine the classifier. During this stage, a confident learning module identifies and selects high-confidence pseudo-labels to optimize the classifier, enhancing its generalizability in the target domain. Thus, TSDA facilitates domain adaptation during both the training and testing stages. Experimental results on two cross-domain datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/Li-ZK/TSDA-2025.