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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045289/ |
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| Summary: | 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 <italic>adaptation</italic> over fine-grained <italic>classification</italic> 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 <italic>classification</italic> and <italic>adaptation</italic>, 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. |
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| ISSN: | 1939-1404 2151-1535 |