Awesome-SAR-ATR-Dataset

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Awesome-SAR-ATR-Dataset

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A brief introduction to the SAR ATR dataset from the 1990s to the 2020s

Awesome-SAR-ATR-Dataset 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# 🛰️ Awesome-SAR-ATR-Dataset

<h5 align="center"><em> Weijie Li (李玮杰) </em></h5>

<p align="center">
  <a href="#Classification">📂 Classification</a> |
  <a href="#Detection">🎯 Detection</a> |
  <a href="#Statement">📄 Statement</a>  
</p>

---

## 📖 Introduction

This is a concise timeline of SAR ATR datasets from the 1990s to the 2020s.

I would like to thank all the researchers for their outstanding contributions. This repository is written to commemorate my PhD journey and to make it easier for future researchers to navigate the field.

If you spot any mistakes or omissions, please feel free to open an issue or contact me at:  
📧 [lwj2150508321@sina.com](mailto:lwj2150508321@sina.com)

---

## 📂 Classification

👉 Please refer to: <a href="Classification.md">Classification.md</a>

---

## 🎯 Detection

👉 Please refer to: <a href="Detection.md">Detection.md</a>

---

## 📄 Statement

If you find our work useful, please give us a star ⭐ on GitHub. You may also be interested in our other research:

---

### 1. ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild  
**📄 TPAMI** | Yongxiang Liu\*, **Weijie Li**, Li Liu\*, Jie Zhou, Bowen Peng, Yafei Song, Xuying Xiong, Wei Yang, Tianpeng Liu, Zhen Liu, Xiang Li\*

- 📝 [Paper (IEEE)](https://ieeexplore.ieee.org/document/11367309/authors#authors)  
- 📚 [BibTeX](./pic/bibtex/Preprint_2025_ATRNet-STAR.txt)  
- 🇨🇳 [Zhihu](https://zhuanlan.zhihu.com/p/1906257303839158589)  
- 💻 [Code](https://github.com/waterdisappear/ATRNet-STAR) [![GitHub stars](https://img.shields.io/github/stars/waterdisappear/ATRNet-STAR?style=social)](https://github.com/waterdisappear/ATRNet-STAR)

> **ATRNet-STAR** contains **40 distinct target types** and is designed to replace the outdated (but widely used) MSTAR dataset, making a significant contribution to SAR ATR research.

---

### 2. SARATR-X: Toward Building a Foundation Model for SAR Target Recognition  
**📄 TIP** | **Weijie Li**, Wei Yang\*, Yunan Hou, Li Liu\*, Yongxiang Liu\*, and Xiang Li

- 📝 [Paper (IEEE)](https://ieeexplore.ieee.org/document/10856784)  
- 📚 [BibTeX](./pic/bibtex/TIP_2025_SARATR-X.txt)  
- 🇨🇳 [Zhihu](https://zhuanlan.zhihu.com/p/20701889782)  
- 💻 [Code](https://github.com/waterdisappear/SARATR-X) [![GitHub stars](https://img.shields.io/github/stars/waterdisappear/SARATR-X?style=social)](https://github.com/waterdisappear/SARATR-X)

> **SARATR-X** is a foundation model that learns generalizable representations via **self-supervised learning** from large‑scale unlabeled data, providing a cornerstone for generic SAR target detection and classification.

---

### 3. Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture  
**📄 ISPRS Journal** | **Weijie Li**, Wei Yang, Tianpeng Liu, Yuenan Hou, Yuxuan Li, Zhen Liu, Yongxiang Liu\*, Li Liu

- 📝 [Paper (ScienceDirect)](https://www.sciencedirect.com/science/article/pii/S0924271624003514)  
- 📚 [BibTeX](./pic/bibtex/ISPRS_Journal_2024_SAR-JEPA.txt)  
- 🇨🇳 [Zhihu](https://zhuanlan.zhihu.com/p/787495052)  
- 💻 [Code](https://github.com/waterdisappear/SAR-JEPA) [![GitHub stars](https://img.shields.io/github/stars/waterdisappear/SAR-JEPA?style=social)](https://github.com/waterdisappear/SAR-JEPA)

> **SAR-JEPA** is a joint‑embedding predictive architecture for SAR ATR that leverages **local masked patches** to predict the **multi‑scale SAR gradient representations** of an unseen context.

---

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