面向不平衡SAR船舶分类的定向过采样增强基础模型
原标题：Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling
AI 摘要
该研究在长尾分布的OpenSARShip SAR船舶数据集上，对遥感基础模型DOFA和SAR-JEPA与ImageNet预训练基线进行基准测试。方法保持骨干网络冻结，仅在嵌入空间对少数类做四种过采样并训练轻量分类头。结果显示过采样提升了Macro-F1和准确率，DOFA用ADASYN从34.39升至38.56，SAR-JEPA用SVM-SMOTE从25.89升至32.30，但部分稀有类仍持续失败。
正文节选
Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling Abstract 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 baselines under a fixed, training-efficient protocol that keeps the backbone frozen. To mitigate imbalance without fi