Addressing SAR ship class imbalance via targeted oversampling of foundation model embeddings

Foundation models pretrained on remote sensing data have shown promise for downstream tasks, yet their behaviour under class imbalance remains underexplored. We benchmark two foundation models, DOFA and SAR-JEPA, against ImageNet-pretrained models on the severely imbalanced OpenSARShip dataset. We apply four feature-space oversampling techniques exclusively to minority classes, scaling them to three times their original size. Our approach achieves up to 8.34% Macro-F1 and 7.34% accuracy improvements over baseline foundation models, demonstrating that targeted oversampling enables better balanced performance on SAR ship classification. We provide code to pre-extract embeddings, and reproducible experiments optimised for free-tier Google Colab https://github.com/cm-awais/SARShipfoundationModels.