UNSUPERVISED DOMAIN ADAPTATION FOR HYPERSPECTRAL IMAGE CLASSIFICATION VIA CAUSAL INVARIANCE

Despite the wide application of deep learning in hyperspectral classification, variations in data collection conditions can lead to domain shift between the training and testing datasets. Traditional hyperspectral classification methods are adversely affected by these distribution differences, resulting in poor generalization performance on the testing set. To overcome this challenge, we present an optimized unsupervised domain adaptation approach based on causal invariance. Our method assumes a causal relationship to reflect the effects of changes in class information and domain information on samples. Based on this causal relationship, we construct a network to separate class-related and domain-related features. To further reduce the negative transfer caused by distribution differences, our model introduces intra-class feature consistency. As a result, our method improves the performance of the model on the target domain. Experimental results on two public hyperspectral datasets demonstrate the superior effectiveness of our method.