AI Research
Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling
Bridging the gap between generalist foundation models and the specific challenges of imbalanced SAR data.
Remote-sensing foundation models offer strong representations for SAR imagery, but their behavior under severe long-tail class imbalance is still not well characterized. We benchmark DOFA and SAR-JEPA on the imbalanced OpenSARShip dataset and compare them with ImageNet-pretrained...
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ArXiv CS.CV
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ArXiv CS.CV
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Addressing class imbalance in remote sensing is critical for accurate ship classification in sparse maritime datasets. By refining foundation models with targeted oversampling, researchers can significantly improve detection reliability for rare vessel types in real-world SAR applications.
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