Awesome-SAR-ATR-Dataset

Weijie Li (李玮杰)

Classification | Detection | Statement

Introduction

It is a brief introduction to the SAR ATR dataset from the 1990s to the 2020s.

Thanks to these researchers for their outstanding work. I write this to honor my PhD journey and make it easier for subsequent researchers.

If it has any mistakes or omissions, please leave an issue or contact me at lwj2150508321@sina.com.

Classification

Please refer to Classification.

Detection

Please refer to Detection.

Statement

And if you find our work is useful, please give us a star 🌟 in GitHub, and you may be interested in our other work:

• ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild (Preprint)

  Yongxiang Liu*, Weijie Li, Li Liu*, Jie Zhou, Bowen Peng, Yafei Song, Xuying Xiong, Wei Yang, Tianpeng Liu, Zhen Liu, Xiang Li*

  [Paper] [BibTex] [知乎] [Code]

  ATRNet-STAR dataset contains 40 distinct target types, collected with the aim of replacing the outdated though widely used MSTAR dataset and making a significant contribution to the advancement of SAR ATR research.

• 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] [BibTex] [知乎] [Code]

  SARATR-X is a foundation model, which learns generalizable representations via self-supervised learning from large-scale unlabelled data and provides a corner stone for generic SAR target detection and classification.

• Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture (ISPRS)

  Weijie Li, Wei Yang, Tianpeng Liu, Yuenan Hou, Yuxuan Li, Zhen Liu, Yongxiang Liu*, Li Liu

  [Paper] [BibTex] [知乎] [Code]

  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.