Synthetic Aperture Radar

Also known as SAR

Synthetic Aperture Radar (SAR) combines microwave echoes collected as a sensor moves to form detailed images, including in darkness and cloudy weather, but images are affected by speckle and viewing geometry. Research develops signal-processing methods such as sparse reconstruction and interferometric SAR (InSAR), alongside deep-learning approaches for recognition, detection, segmentation, and change mapping. CNNs, Transformers, state-space models, self-supervised pretraining, and physics-informed scattering priors are used to learn from limited labels and transfer across sensors; optical imagery and generative models also support cross-modal tasks. A central challenge is balancing robust performance across sensors, terrain, and acquisition conditions with physical fidelity, efficient computation, and reliable evaluation on real-world data.

Latest papers

70 papers4 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

PASTE: Physics-Aware Scattering Topology Embedding Framework for SAR Object Detection
Mar 16, 2026Jiacheng Chen, Yuxuan Xiong, Haipeng WangObject Detection Synthetic Aperture Radar
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China
ALTIS: Automated Loss Triage and Impact Scoring from Sentinel-1 SAR for Property-Level Flood Damage Assessment
Mar 14, 2026Amogh Vinaykumar, Prem KamasaniDisaster Damage Assessment Synthetic Aperture Radar
Altis Intelligence
SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification
Mar 13, 2026Furui Chen, Han Wang, Yuhan Sun +5Cross-Modal Representation Learning Cross-Modal Retrieval
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · University of Chinese Academy of Sciences, Beijing 100049, China +1
OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection
Mar 10, 2026Shuaiyu Chen, Ming Yin, Peng Ren +2Synthetic Aperture Radar Remote Sensing Image Segmentation
Multimodal Intelligence Lab, Department of Computer Science, University of Exeter, United Kingdom · College of Oceanography and Space Informatics, China University of Petroleum (East China), China. · Department of Computer Science, University of Exeter, United Kingdom
Online Sparse Synthetic Aperture Radar Imaging
Mar 9, 2026Conor Flynn, Radoslav Ivanov, Birsen YaziciSynthetic Aperture Radar Streaming Algorithms
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180 USA · Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180 USA
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
Neural Implicit Representations for 3D Synthetic Aperture Radar Imaging
Feb 19, 2026Nithin Sugavanam, Emre ErtinSynthetic Aperture Radar 3D Reconstruction
Ohio State University, Columbus, OH 43210, USA
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
C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation
Nov 16, 2024Jeonghyeok Do, Jaehyup Lee, Munchurl KimImage-to-Image Translation Latent Diffusion Models
KAIST · KNU
SAR image segmentation algorithms based on I-divergence-TV model
Dec 9, 2023Guangming LiuImage Segmentation Remote Sensing Image Segmentation
Yantai Science and Technology Association

Mar 16, 2026J/K move · Enter open · S save

PASTE: Physics-Aware Scattering Topology Embedding Framework for SAR Object Detection

Jiacheng Chen, Yuxuan Xiong, Haipeng Wang

Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China

Object Detection Synthetic Aperture Radar Physics-Informed ML

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Current deep learning-based object detection for Synthetic Aperture Radar (SAR) imagery mainly adopts optical image methods, treating targets as texture patches while ignoring inherent electromagnetic scattering mechanisms. Though scattering points have been studied to boost detection performance, most methods still rely on amplitude-based statistical models. Some approaches introduce frequency-domain information for scattering center extraction, but they suffer from high computation cost and poor compatibility with diverse datasets. Thus, effectively embedding scattering topological information into modern detection frameworks remains challenging. To solve these problems, this paper proposes the Physics-Aware Scattering Topology Embedding Framework (PASTE), a novel closed-loop architecture for comprehensive scattering prior integration. By building the full pipeline from topology generation, injection to joint supervision, PASTE elegantly integrates scattering physics into modern SAR detectors. Specifically, it designs a scattering keypoint generation and automatic annotation scheme based on the Attributed Scattering Center (ASC) model to produce scalable and physically consistent priors. A scattering topology injection module guides multi-scale feature learning, and a scattering prior supervision strategy constrains network optimization by aligning predictions with scattering center distributions. Experiments on real datasets show that PASTE is compatible with various detectors and brings relative mAP gains of 2.9% to 11.3% over baselines with acceptable computation overhead. Visualization of scattering maps verifies that PASTE successfully embeds scattering topological priors into feature space, clearly distinguishing target and background scattering regions, thus providing strong interpretability for results.