Boosting Universal Domain Adaptation in Remote Sensing With Dual-Classifiers Consistency Discrimination and Cross-Domain Feature Mixup

In the field of remote sensing image classification, domain adaptation (DA) methods have been extensively utilized to overcome the challenges posed by data discrepancies between source and target domains that arise from varying imaging conditions, sensor differences, or geographical variations. Stemming from the existence of unseen classes in both the source and target domains, universal DA (UniDA) poses the greatest challenge that demands innovative solutions. Existing UniDA methods often overlook intra-domain variations within the target domain and face difficulties in distinguishing between similar known and unknown classes, which significantly hinder cross-domain transfer. To overcome these challenges, we propose a dual-classifier network tailored for cross-domain classification of remote sensing images, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DCmix</i>. DCmix introduces a dual-classifiers network that utilizes both closed-set and open-set classifiers to improve the accuracy of identifying unknown sample classes. To our knowledge, this is the first attempt to introduce dual classifiers into the UniDA remote sensing image classification task. We further enhance the feature generalization capability of the target domain based on sample neighborhood relations, resulting in a more adaptable and robust feature representation. A cross-domain feature mixup scheme is also designed based on the consistency discrimination of the dual classifiers, achieving smoother decision boundaries and simpler hidden layer representations. Extensive experiments conducted on four hyperspectral image datasets and three RGB datasets prove that the introduced approach attains state-of-the-art performance in remote sensing image classification under the UniDA scenario.