# Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

arXiv: https://arxiv.org/abs/2007.02106 · HTML: https://mlsanity.com/paper/2007.02106/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2311.15153/) (2023)
- [SARATR-X: Toward Building A Foundation Model for SAR Target Recognition](https://mlsanity.com/paper/2405.09365/) (2024)
- [Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense](https://mlsanity.com/paper/2209.04779/) (2022)
- [ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild](https://mlsanity.com/paper/2501.13354/) (2025)
- [Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR](https://mlsanity.com/paper/2304.00668/) (2023)
- [Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition](https://mlsanity.com/paper/2304.03550/) (2023)
- [SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs](https://mlsanity.com/paper/2407.00851/) (2024)
- [Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR](https://mlsanity.com/paper/2401.17038/) (2024)
- [Exploring Syn-to-Real Domain Adaptation for Military Target Detection](https://mlsanity.com/paper/2512.23208/) (2025)
- [Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition](https://mlsanity.com/paper/2304.01747/) (2023)
- [Background Debiased SAR Target Recognition via Causal Interventional Regularizer](https://mlsanity.com/paper/2308.15724/) (2023)
- [Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation](https://mlsanity.com/paper/2308.16633/) (2023)
- [Multi-Base Station Cooperative Sensing with AI-Aided Tracking](https://mlsanity.com/paper/2310.20403/) (2023)
- [Energy Score-based Pseudo-Label Filtering and Adaptive Loss for Imbalanced Semi-supervised SAR target recognition](https://mlsanity.com/paper/2411.03959/) (2024)
- [Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion](https://mlsanity.com/paper/2509.09298/) (2025)
- [SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition](https://mlsanity.com/paper/2601.10324/) (2026)
- [Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data](https://mlsanity.com/paper/2604.14229/) (2026)
- [A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation](https://mlsanity.com/paper/2606.28896/) (2026)

## Abstract (arXiv)

Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along with a direct comparison of the strengths and weaknesses of each method under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset we also highlight its weaknesses and suggest future research directions.