An Adversarial Approach to Cross-Sensor Hyperspectral Data Classification

Domain invariant representation learning is one of the domain adaptation techniques, which aims at learning a representation that is not affected by a possible shift in the data distribution between training and test sets. Traditional domain adaptation methods proposed in the literature focus on first learning a domain invariant transformation from the input space to a new representation space and then training a classifier in the new space. Alternatively, in this paper, we propose an adversarial domain adaptation technique that combines representation learning, domain adaptation and classifier learning in a single training process. Moreover, the proposed method performs the domain adaptation by using the Wasserstein metrics to minimize the domain discrepancy. We applied the proposed method on a hyperspectral image classification problem and the results obtained show the effectiveness of the method.