SAR ATR

ATR: Automatic Target Recognition · SAR: Synthetic Aperture Radar

Latest papers

13 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

LoRA Enhanced Contrastive Learning with SAS Vision Transformers
Sep 17, 2026Dan Zimmerman, Frank E. Bobe, Amelia L. McCormack +2SAR ATR Underwater Object Detection
Center for Connected Autonomy & AI Florida Atlantic University Boca Raton, USA · Naval Surface Warfare Center Panama City Division Panama City, USA · Department of Computer Science Florida State University Tallahassee, USA
Cross-modal learning for SAR target recognition using optical vision foundation models
Sep 7, 2026Lucas Hirsch, James R. Hopgood, Javid Khan +2Cross-Modal Alignment Cross-Modal Representation Transfer
School of Engineering, University of Edinburgh, United Kingdom · Leonardo UK, United Kingdom · School of Engineering and Physical Sciences, Heriot-Watt University, United Kingdom
Fast Generation of Representative Synthetic Dataset with Salsa to Train ATR Models with Electromagnetic Couplings Data-Augmentation
Jul 21, 2026Benjamin Camus, Julien Houssay, Corentin Le Barbu +3Synthetic Data Generation Synthetic Aperture Radar
DGA.MI · Scalian DS 2 rue Antoine Becquerel 35700 Rennes, France · DGA Maîtrise de l’Information BP 7 35998 Rennes CEDEX 9, France
A Generalized Deep Non-negative Matrix Factorization Approach for SAR Automatic Target Recognition
Jul 8, 2026Yunhong Zhang, Changjie Cao, Zhongli Zhou +4Non-Negative Matrix Factorization Remote Sensing
SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition
Jun 30, 2026Ke Wang, Xiaoyi Pan, Zhaoyu Gu +4Mamba Self-Supervised Pre-Training
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
Jun 3, 2026Fan Zhang, Sijin Zheng, Fei Ma +4Class-Incremental Learning Remote Sensing
School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China · School of Information Science and Technology and the Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, China · Beihang University, Beijing 100191, China +1
Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification
May 15, 2026Daniel Brignac, Fengwei Tian, Banafsheh Latibari +2Adversarial Robustness Adversarial Attacks
The University of Arizona Tucson, Arizona, United States
Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition
May 11, 2026David F. Ramirez, Tim L. Overman, Kristen Jaskie +2Remote Sensing Image Understanding Remote Sensing VQA
SenSIP Center, School of ECEE, Arizona State University, Tempe, Arizona · Prime Solutions Group, Goodyear, Arizona
Online CS-based SAR Edge-Mapping
Apr 21, 2026Conor Flynn, Radoslav Ivanov, Birsen YaziciEdge Detection Remote Sensing
Rensselaer Polytechnic Institute, Troy, New York, USA
Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Apr 14, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanSAR ATR Quantum Data Encoding
Dept. of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, India
FSCE: A Target-Aware Frequency-Spatial Collaborative Enhancement Framework for Noise-Resilient SAR ATR
Mar 23, 2026Yansong Lin, Zihan Cheng, Ziyue Yang +4Image Corruption Robustness Frequency-Domain Feature Learning
Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, China · School of Computer Science and Engineering (School of Artificial Intelligence), Wuhan Institute of Technology, China · Institute of Automation, Chinese Academy of Sciences, China +1
RSTNet: Enhancing Small-Target Recognition in Noisy SAR Imagery via Robust Feature Learning and Distribution-Aware Regression
Feb 27, 2026Xiaojing Zhao, Shiyang Li, Zenan Chu +5Synthetic Aperture Radar Small Object Detection
College of Mathematics and Information Science, Anyang Institute of Technology, Anyang, 455000, China · School of Computer Science and Information Engineering, Anyang Institute of Technology, Anyang, 455000, China · College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, China +3
Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition
Dec 31, 2025Yuchao Hou, Zixuan Zhang, Jie Wang +9Adversarial Training Remote Sensing
Shanxi Normal University, Taiyuan, China · Guizhou University, Guiyang, China · Shanxi Key Laboratory of Cryptography and Data Security, School of Computer Science and Artificial Intelligence, Shanxi Normal University, Taiyuan 030031, China +15

Sep 17, 2026J/K move · Enter open · S save

LoRA Enhanced Contrastive Learning with SAS Vision Transformers

Dan Zimmerman, Frank E. Bobe, Amelia L. McCormack, Matthew Cook+1

Center for Connected Autonomy & AI Florida Atlantic University Boca Raton, USA · Naval Surface Warfare Center Panama City Division Panama City, USA · Department of Computer Science Florida State University Tallahassee, USA

SAR ATR Underwater Object Detection Underwater Imaging Low-Rank Adaptation Underwater Acoustics

Open paper AskPDF arXiv

Automatic target recognition (ATR) with synthetic aperture sonar (SAS) supports advanced naval capabilities, but deep learning is constrained by scarce target imagery, background clutter, and human-in-the-loop assessment. We adapt DINOv3 Vision Transformer (ViT) models to underwater SAS ATR using a three-stage parameter-efficient framework. Stage 1 uses Low-Rank Adaptation (LoRA) while freezing the ViT backbone, bridging the gap between natural-image pretraining and underwater acoustic propagation. Stage 2 uses hard-negative mining to strengthen the decision boundary against acoustic mimics, including rocks and sediment formations resembling man-made targets. Stage 3 uses Supervised Contrastive Learning (SupCon) to separate target and clutter representations. We evaluate at-sea SAS data using a mission-level geographic split, compare all arms at 85 percent test recall, and repeat each comparison over three random seeds. LoRA accounts for the primary effect, increasing area under the precision-recall curve (AUPRC) from 0.300 to 0.679 +/- 0.027 using the same frozen backbone. Rank 4 achieves this result while training only 0.26 percent of weights. Neither refinement stage exceeds its matched control: hard-negative mining changes AUPRC by -0.0045 +/- 0.0119 versus an equal-size random curriculum, and SupCon changes AUPRC by +0.0002 +/- 0.0096 versus the preceding stage. These null results indicate that mining occurred on data the encoder had already fit and that supervised stages had already imposed most target-clutter geometry. One efficient adaptation stage is sufficient; stacked refinement is not.