A framework for imbalanced SAR ship classification: curriculum learning, weighted loss functions, and a novel evaluation metric

Accurate ship classification is essential for maritime traffic monitoring applications but is significantly hindered by imbalanced datasets. In this paper, we propose a novel methodology that combines curriculum learning with weighted loss functions to address class imbalance in the FUSAR-Ship dataset, facilitating the accurate classification of its nine classes. Our method achieved notable improvements, including a 6.53% average increase in F1-scores compared to baseline models, and successfully identified all classes, including previously misclassified ones. To better evaluate model performance on long-tailed datasets, we introduce a novel evaluation metric that provides a more nuanced assessment of classification ability across underrepresented classes. While demonstrated on the FUSAR-Ship dataset, our approach and metric are broadly applicable to other imbalanced classification problems.