Universal Domain Adaptation without Source Data for Remote Sensing Image Scene Classification

Abstract

Existing domain adaptation (DA) approaches are usually not well suited for practical DA scenarios of remote sensing image classification, since these methods (such as unsupervised DA) rely on rich prior knowledge about the relationship between label sets of source and target domains, and source data are usually not accessible in many cases due to the privacy or confidentiality issues. To this end, we propose a novel source data generation-based universal domain adaptation (SDG-UniDA) model, which includes two parts, i.e., the stage of source data generation and the stage of model adaptation. The first stage is to estimate the conditional distribution of source data from the pre-trained model using the knowledge of class-separability in the source domain and then to synthesize the source data. With this synthetic source data in hand, it becomes a universal DA task that requires no prior knowledge on the label sets. A novel transferable weight is proposed to distinguish the shared and private label sets to each domain, thereby promoting the adaptation in the automatically discovered shared label set and recognizing the “unknown” samples successfully. Empirical results show that SDG-UniDA is effective and practical in this challenging setting for remote sensing image scene classification.