Sample-prototype optimal transport-based universal domain adaptation for remote sensing image classification

In recent years, there is a growing interest in domain adaptation for remote sensing image scene classification, particularly in universal domain adaptation, where both source and target domains possess their unique private categories. Existing methods often lack precision on remote sensing image datasets due to insufficient prior knowledge between the source and target domains. This study aims to effectively distinguish between common and private classes despite large intra-class sample discrepancies and small inter-class sample discrepancies in remote sensing images. To address these challenges, we propose Sample-Prototype Optimal Transport-Based Universal Domain Adaptation (SPOT). The proposed approach comprises two key components. Firstly, we utilize an unbalanced optimal transport algorithm along with a sample complement mechanism to identify common and private classes based on the optimal transport assignment matrix. Secondly, we leverage the optimal transport algorithm to enhance discriminability among different classes while promoting similarity within the same class. Experimental results demonstrate that SPOT significantly enhances classification accuracy and robustness in universal domain adaptation for remote sensing images, underscoring its efficacy in addressing the identified challenges.