A Survey on SAR Ship Classification Using Deep Learning

Abstract

Deep learning (DL) has become a central approach for ship classification using synthetic aperture radar (SAR) imagery. This survey reviews 74 representative studies selected from 187 publications, categorizing them into a taxonomy with four main dimensions: 1) DL architectures; 2) datasets; 3) image augmentation; and 4) learning techniques. We analyze how approaches such as handcrafted feature integration, data augmentation, fine-tuning, and transfer learning influence classification performance, and summarize the use of public benchmarks including OpenSARShip and FUSARShip. This survey highlights key challenges: limited data availability, class imbalance, lack of standardized metrics, and limited interpretability of DL models. Future research directions include the development of SAR-specific DL architectures, advanced augmentation and generative approaches, integration of handcrafted and deep features, interpretable DL, and stronger interdisciplinary collaboration. By addressing these challenges, DL-based SAR ship classification can achieve greater robustness, accuracy, and transparency, ultimately strengthening maritime surveillance and operational monitoring.