Entropy-Guided Weighted Adversarial Open-Set Domain Adaptation Method for Hyperspectral Image Classification

Closed Set Domain Adaptation (CSDA) assumes identical class sets between source and target domains and is an important solution for reducing domain bias. Compared to CSDA, Open Set Domain Adaptation (OSDA) is closer to realworld applications by allowing unknown class samples in the target domain. In addition, previous OSDA methods mainly rely on similarity detection between the target and source domains to identify unknown classes, which does not fully capture the characteristics of the target domain. To address these limitations, this letter proposes an open set domain adaptation method integrating entropy-guided weighted adversarial networks and contrastive self-supervised learning for hyperspectral image (HSI) classification. The approach introduces an entropy-guided weighted adversarial network to distinguish between known and unknown classes in the target domain, while weighing their importance for aligning the feature distributions. Contrastive self-supervised learning is introduced to learn the intrinsic structure and discriminative features of the target domain from unlabeled target domain data. Experimental validation on two HSI cross-domain datasets demonstrates significant performance improvements over existing methods.