Unsupervised SAR Fine-Grained Ship Classification via Spherical Metric Refinement With Deep Subdomain Adaptation

Unsupervised domain adaptation (UDA) is a promising method for addressing the problem of SAR fine-grained ship classification in target domain with no labeled data available by leveraging a large number of labeled samples from source domains. This article proposes a novel framework, spherical metric refinement with deep subdomain adaptation, to address two crucial issues that are rarely recognized by existing UDA approaches, namely prioritizing adaptation over fine-grained classification and hindering cross-domain alignment and discrimination due to Euclidean feature norms. The proposed solution transforms features into spherical space to eliminate norm bias and introduces joint optimization of classification and adaptation, balancing discriminative feature learning and domain invariance. Experiments on GF-SAR and HR-SAR datasets demonstrate state-of-the-art performance, achieving 95.33% and 89.33% classification accuracy, respectively, outperforming the existing methods by 5.33–6.00%. Our GF-SAR and HR-SAR datasets have been released on GitHub.