# A Survey on SAR ship classification using Deep Learning

arXiv: https://arxiv.org/abs/2503.11906 · HTML: https://mlsanity.com/paper/2503.11906/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 134 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up](https://mlsanity.com/paper/2102.07074/) (2021)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation](https://mlsanity.com/paper/2403.15356/) (2024)
- [Foundation Models for Remote Sensing and Earth Observation: A Survey](https://mlsanity.com/paper/2410.16602/) (2024)
- [Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2311.15153/) (2023)
- [A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution](https://mlsanity.com/paper/2305.02074/) (2023)
- [Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification](https://mlsanity.com/paper/2311.15202/) (2023)
- [Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification](https://mlsanity.com/paper/2311.15231/) (2023)
- [A Dual-Polarization Information Guided Network for SAR Ship Classification](https://mlsanity.com/paper/2207.04639/) (2022)
- [A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction](https://mlsanity.com/paper/2306.15369/) (2023)

## Built on by (most cited first, 5 of 5)

- [NASTaR: NovaSAR Automated Ship Target Recognition Dataset](https://mlsanity.com/paper/2512.18503/) (2025)
- [Efficient SAR Vessel Detection for FPGA-Based On-Satellite Sensing](https://mlsanity.com/paper/2507.04842/) (2025)
- [Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization](https://mlsanity.com/paper/2510.11449/) (2025)
- [Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling](https://mlsanity.com/paper/2609.31657/) (2026)
- [Cross-Dataset Transfer and Unknown-Class Detection in Imbalanced SAR Ship Classification](https://mlsanity.com/paper/2609.31658/) (2026)

## Abstract (arXiv)

Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. This survey comprehensively analyzes the diverse DL techniques employed in this domain. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques, and fostering interdisciplinary collaborations to improve DL model performance. This survey establishes a first-of-its-kind taxonomy for categorizing relevant research based on DL models, handcrafted feature use, SAR attribute utilization, and the impact of fine-tuning. We discuss the methodologies used in SAR ship classification tasks and the impact of different techniques. Finally, the survey explores potential avenues for future research, including addressing data scarcity, exploring novel DL architectures, incorporating interpretability techniques, and establishing standardized performance metrics. By addressing these challenges and leveraging advancements in DL, researchers can contribute to developing more accurate and efficient ship classification systems, ultimately enhancing maritime surveillance and related applications.