SAR Automatic Target Recognition
ATRNet-STAR: Depression Angle Diversity, Few-Shot Recognition, and Cross-Dataset Generalization in SAR Automatic Target Recognition
Mousa Shaya · Shaya Software and Systems LLC | Arizona State University
arXiv preprint, May 2026
Overview
Synthetic Aperture Radar (SAR) enables all-weather, day/night target recognition — a critical capability for defense and intelligence applications. This study systematically evaluates SAR ATR generalization across three axes that matter operationally but are rarely studied together: depression angle variation, data scarcity (few-shot), and cross-dataset transfer.
The work uses two benchmarks: ATRNet-STAR (40 vehicle classes across 4 depression angles: 15°, 30°, 45°, 60°) and MSTAR (10 vehicle classes, standard operating conditions and extended operating conditions).
Key Results
| Experiment | Accuracy |
|---|---|
| SOC baseline (ConvNeXt-Small) | 97.2% |
| EOC — 15° only model, tested at other angles | 43.7% |
| EOC — adding 60° to training | 74.1% (+30.4 pp) |
| Prototypical (5 shots per class) | 98.2% |
| MSTAR (cross-dataset transfer) | 99.1% |
| Cross-dataset transfer (ATRNet→MSTAR) | 61.1% |
| DINOv2 optical features on SAR | 6.1% (near-chance) |
| Test-time augmentation (TTA) | −24.3 pp vs. baseline |
Key finding: Depression angle is the dominant challenge in operational SAR ATR. A model trained on a single angle collapses to 43.7% at other angles. Adding multi-angle training data is more effective than any architectural improvement. Optical foundation model features (DINOv2) are near-useless on SAR — SAR requires SAR-trained features.
Feature Space Analysis (UMAP)
The figures below show the 768-dimensional ConvNeXt feature space projected to 2D via UMAP, color-coded by vehicle class.
(UMAP figures will appear here — images in /assets/images/projects/sar-atr/)
Methods
Backbone: ConvNeXt-Small pretrained on ImageNet-1k, fine-tuned on single-channel SAR chips (64×64 px, log-amplitude normalized).
Few-shot: Prototypical networks — class prototypes computed as mean of support embeddings; test samples classified by nearest prototype in embedding space.
Evaluation: Standard Operating Conditions (SOC) follow the MSTAR convention. Extended Operating Conditions (EOC) test across depression angles not seen during training.
Cross-dataset: Model trained on ATRNet-STAR, evaluated zero-shot on MSTAR (no MSTAR training data used).
Datasets
| Dataset | Classes | Angles | Resolution | Size |
|---|---|---|---|---|
| ATRNet-STAR | 40 | 15°, 30°, 45°, 60° | ~0.3m | ~100K chips |
| MSTAR | 10 | 15°, 17° | ~0.3m | ~5K chips |
Citation
@article{shaya2026atrnetstar,
title={ATRNet-STAR: Depression Angle Diversity, Few-Shot Recognition,
and Cross-Dataset Generalization in SAR Automatic Target Recognition},
author={Shaya, Mousa},
journal={arXiv preprint arXiv:2506.XXXXX},
year={2026}
}