Unsupervised Domain Adaption of Hyperspectral Images Based on Paring Domain Discrimination

Unsupervised domain adaptation (UDA) reduces domain shifts between distributions to enable model generalization to new scenarios. Adversarial domain adaptation (DA) is an effective approach that extracts domain-invariant features through adversarial learning, but such methods often neglect the influence of category differences on domain discrimination. To solve this problem, we construct a new unsupervised domain adaption hyperspectral image (HSI) classification method. The proposed method consists of two modules, namely, the pairing domain discrimination learning module and the multilevel mutual information maximization module. We propose to construct the sample pair as the input of the domain discriminator. We introduce a new label to the sample pair according to the labels of the two samples and use the relationship between samples to reduce the impact of sample category differences on domain discrimination. When extracting the shared features of the two domains, it will inevitably cause the loss of task-related information. This information is retained by maximizing the proposed multilevel mutual information. The experimental results on different datasets show the effectiveness of our method.