Open-Set Domain Adaptation for Hyperspectral Image Classification Based on Weighted Generative Adversarial Networks and Dynamic Thresholding

Recent studies have shown that the deep domain adaptation (DA) technique has achieved remarkable results in cross-domain hyperspectral image (HSI) classification task. However, these DA methods assume that the source and target domains share the same classes, which may not hold true in real-world applications. Under open-set conditions, since the target domain may contain classes unseen in the source domain, direct domain alignment can lead to negative transfer phenomena. Moreover, the presence of multiple unknown classes in the target domain makes it difficult to learn more discriminative classification boundaries between known and unknown classes. To address these issues, we propose an open-set DA (OSDA) method for HSI classification based on weighted generative adversarial networks and dynamic thresholding (WGDT). First, we introduce a class anchor (CA) strategy to learn the metric space of known classes in the source domain. By calculating the similarity between the target-domain samples and the CA, we compute the reliability weights of the samples belonging to known classes. Then, based on these weights, we design an instance-level weighted-domain adversarial learning strategy to better align samples that are more likely to belong to known classes, avoiding negative transfer phenomena. Finally, we propose a dynamic thresholding method to learn the classification boundaries between known and unknown classes in the feature space and reject unknown class samples, thereby separating known class samples in the target domain. The experimental results on four cross-scene HSI classification tasks demonstrate that our proposed method outperforms some existing methods. The code is available at https://github.com/Li-ZK/WGDT.