# Towards SAR Automatic Target Recognition MultiCategory SAR Image Classification Based on Light Weight Vision Transformer

arXiv: https://arxiv.org/abs/2407.06128 · HTML: https://mlsanity.com/paper/2407.06128/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 13 of 13)

- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition](https://mlsanity.com/paper/2105.15075/) (2021)
- [VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding](https://mlsanity.com/paper/2312.08733/) (2023)
- [Pareto Self-Supervised Training for Few-Shot Learning](https://mlsanity.com/paper/2104.07841/) (2021)
- [MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning](https://mlsanity.com/paper/2312.08636/) (2023)
- [Real-Time Pill Identification for the Visually Impaired Using Deep Learning](https://mlsanity.com/paper/2405.05983/) (2024)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans](https://mlsanity.com/paper/2010.00641/) (2020)
- [Attention Hijacking in Trojan Transformers](https://mlsanity.com/paper/2208.04946/) (2022)
- [Compressed Meta-Optical Encoder for Image Classification](https://mlsanity.com/paper/2406.06534/) (2024)
- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks](https://mlsanity.com/paper/2106.14344/) (2021)
- [A Robust Adversary Detection-Deactivation Method for Metaverse-oriented Collaborative Deep Learning](https://mlsanity.com/paper/2401.01895/) (2023)

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

- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)
- [A Hybrid Transformer-Mamba Network for Single Image Deraining](https://mlsanity.com/paper/2409.00410/) (2024)
- [Neural Tangent Knowledge Distillation for Optical Convolutional Networks](https://mlsanity.com/paper/2508.08421/) (2025)
- [SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2506.14791/) (2025)
- [SAR-RAG: ATR Visual Question Answering by Semantic Search, Retrieval, and MLLM Generation](https://mlsanity.com/paper/2602.04712/) (2026)

## Abstract (arXiv)

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large scene data intensive technology puts a high value on automatic target recognition which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network and convolutional neural network varied models to deepen the regime's understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer based model to classify SAR images. The entire structure was verified by an open-accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.