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

Awais Ch Muhammad, Reggiannini M., Moroni D.

SAR ship classification, Deep learning, Imbalanced dataset, Loss function, Evaluation metrics 

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.

Publisher: IEEE

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