Synthetic Aperture Radar (SAR) Applications and Techniques

Últimos artículos

• Bridging Modalities and Tasks: A Unified Hierarchical ViT for SAR-to-Optical Translation and Semantic Segmentation

  Siyuan Liu, Xuze Zhang, Yongshun Wang, Licong Pan, Hang Liu, Huihui Li · 7 de septiembre de 2026

  Synthetic Aperture Radar (SAR) images have all-weather, day-and-night observation capabilities. However, compared with optical images, their speckle noise and non-intuitive scattering mechanism limit the interpretability of the images. Generative models for SAR-to-optical (S2O) conversion can improv…

• C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation

  Jeonghyeok Do, Jaehyup Lee, Munchurl Kim · 2 de septiembre de 2026

  Synthetic Aperture Radar (SAR) imagery provides robust environmental and temporal coverage (e.g., during clouds, seasons, day-night cycles), yet its noise and unique structural patterns pose interpretation challenges, especially for non-experts. SAR-to-EO (Electro-Optical) image translation (SET) ha…

• To Remove or Not to Remove Clouds: A Comparative Analysis and Fusion of Raw SAR and Synthetic NDWI for Overcast Water Segmentation

  Saleh Sakib Ahmed, Sara Nowreen, M. Sohel Rahman · 19 de agosto de 2026

  Persistent clouds blind optical satellites during floods. While Synthetic Aperture Radar (SAR) penetrates clouds, its raw data is noisy and lacks clear contrast. To mitigate this, recent studies utilize deep learning models to translate SAR into cloud-free synthetic optical imagery for downstream ta…

• Geometry-Calibrated Closed-Form Shrinkage for SAR Despeckling

  Xuran Hu, Mingzhe Zhu, Djordje Stanković, Yujie Zhu, Zhenpeng Feng, Yifang Ban, Ljubiša Stanković · 18 de agosto de 2026

  Synthetic aperture radar (SAR) despeckling is an inverse-recovery problem in which multiplicative non-Gaussian noise must be suppressed without erasing scattering structures. We revisit a nonlocal sparse estimator that applies a log--Yeo--Johnson transformation, stacks similar patches into groups, c…

• WGDnet: Wishart-guided Geometric-aware Deep Network for PolSAR Image Classification

  Junfei Shi, Haojia Zhang, Yu Cheng, Yuke Li · 28 de julio de 2026

  Polarimetric Synthetic Aperture Radar (PolSAR) classification underpins all-weather Earth observation. Conventional Wishart methods depend on rigid handcrafted operators with limited adaptability, while mainstream deep networks ignore PolSAR native Wishart scattering statistics. Additionally, fixed …

• Direction-adaptive Mamba: Spatial-Frequency Dual-Domain Collaborative Learning for PolSAR Image Classification

  Junfei Shi, Yu Cheng, Haojia Zhang, Wenqiang Hua, Junhuai Li, Maoguo Gong · 28 de julio de 2026

  Deep learning dominates polarimetric synthetic aperture radar (PolSAR) image classification, with Mamba architectures serving as favorable backbones due to linear complexity and strong global modeling capacity. However, existing PolSAR Mamba methods have two critical flaws: pure spatial processing d…

• Physics-Aware Complex-Valued State Space Model with Scattering-Prior Feature Modulation for PolSAR Image Classification

  Fangyan Zhang, Fan Zhang, Shiqi Zhou, Jun Ni, Carlos López-Martínez, Qiang Yin · 23 de julio de 2026

  Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms. Many existing complex-valued networks can preserve amplitude-phase information, but they ar…

• Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data

  Luiz Felipe Parente Santiago (Institute of Computing, Brazilian Army Research Institute in the Amazon), Rosiane Rodrigues de Freitas (Institute of Computing), Daniel Rodrigues dos Santos (Military Institute of Engineering), Felipe Ferrari (Military Institute of Engineering) · 7 de julio de 2026

  The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery. To overcome these phy…

• Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning

  Cade W. Trotter, Maksim E. Eren, Justin C. Holmes, J. Brent Parham, David Ewing, Boian S. Alexandrov, Gian Luca Delzanno · 23 de junio de 2026

  Radio-frequency (RF) monitoring is essential for space domain awareness, but it often generates large, variable, and sparsely populated datasets with few labels. These observations can capture satellites, space debris, and the ionospheric background, yet interpreting them typically requires speciali…

• WaveDINO: Learning-Based Atmospheric Correction of Unwrapped InSAR Interferograms Validated by GNSS: Results at Laguna del Maule and Campi Flegrei Volcanoes

  Robert Popescu, Juliet Biggs, Tianyuan Zhu, Nantheera Anantrasirichai · 16 de junio de 2026

  Interferometric Synthetic Aperture Radar (InSAR) enables effective monitoring of volcanic deformation; however, the observed signals are often corrupted by atmospheric phase delays, seasonal surface changes, and decorrelation effects. Existing atmospheric correction methods, such as numerical weathe…

• Beyond Backscatter: InSAR coherence from detected SAR images

  Francescopaolo Sica, Andrea Pulella, Michael Schmitt · 8 de junio de 2026

  In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistration. A Residual U-Net is trained using coherence maps derived from precisely coregistered Sentinel-1 SLC data to learn the relationship between back…

• T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction

  Kerod Woldesenbet, Abem Woldesenbet · 5 de junio de 2026

  We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder from SAR-JEPA is domain-adapted on 39,300 Capella patches using local masked reconstruction with gradient feature prediction. A temporal transformer …

• DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection

  Arun Sharma · 2 de junio de 2026

  Dark vessel detection requires fusing what vessels report through AIS with what satellites observe through radar and optical sensors. DarkVesselNet is a multi-modal remote sensing stack that combines Sentinel-1 SAR, Sentinel-2 optical imagery, geospatial foundation model backbones, AIS trajectory re…

• PolSAR Image Classification using a Hybrid Complex-Valued Network (HybridCVNet)

  Mohammed Q. Alkhatib · 1 de junio de 2026

  Recently, convolutional neural networks (CNNs) have become popular for image classification due to their effectiveness in computer vision tasks. Now, researchers are exploring the potential of vision transformers (ViTs) in remote sensing and Earth observation. However, traditional Real-Valued networ…

• Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data

  Islam Mansour, Ronny Haensch, Irena Hajnsek, Konstantinos Papathanassiou · 21 de mayo de 2026

  Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data. In this context, a ML model for estimating forest height from TanDEM-X interferometric coherence measurements has recently been proposed, that con…

• Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data

  Arsalaan Ahmad, Oktay Karakus, Paul L. Rosin · 12 de mayo de 2026

  Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic inputs whose contributions remain poorly understood. The question of which channels are actually necessary has received surprisingly little attention. …

• When Less Is More: Simplicity Beats Complexity for Physics-Constrained InSAR Phase Unwrapping

  Prabhjot Singh, Manmeet Singh · 5 de mayo de 2026

  Operational phase unwrapping is the primary computational bottleneck in InSAR-based volcanic and seismic monitoring. We challenge the industry trend of adopting high-complexity computer vision architectures, such as attention mechanisms, without validating their suitability for physics-constrained g…

• Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series

  Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer · 23 de abril de 2026

  The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deployment and operation at global scale. While Earth Observation based offshore wind infrastructure mapping has matured for spatial localization, existing…

• DDF2Pol: A Dual-Domain Feature Fusion Network for PolSAR Image Classification

  Mohammed Q. Alkhatib · 22 de abril de 2026

  This paper presents DDF2Pol, a lightweight dual-domain convolutional neural network for PolSAR image classification. The proposed architecture integrates two parallel feature extraction streams, one real-valued and one complex-valued, designed to capture complementary spatial and polarimetric inform…

• WILD-SAM: Phase-Aware Expert Adaptation of SAM for Landslide Detection in Wrapped InSAR Interferograms

  Yucheng Pan, Heping Li, Zhangle Liu, Sajid Hussain, Bin Pan · 17 de abril de 2026

  Detecting slow-moving landslides directly from wrapped Interferometric Synthetic Aperture Radar (InSAR) interferograms is crucial for efficient geohazard monitoring, yet it remains fundamentally challenged by severe phase ambiguity and complex coherence noise. While the Segment Anything Model (SAM) …

• HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation

  Yongxiang Liu, Jie Zhou, Yafei Song, Tianpeng Liu, Li Liu · 14 de abril de 2026

  Synthetic Aperture Radar (SAR) imagery generation is essential for deepening the study of scattering mechanisms, establishing trustworthy electromagnetic scene models, and fundamentally alleviating the data scarcity bottleneck that constrains development in this field. However, existing methods find…

• An InSAR Phase Unwrapping Framework for Large-scale and Complex Events

  Yijia Song, Juliet Biggs, Alin Achim, Robert Popescu, Simon Orrego, Nantheera Anantrasirichai · 24 de marzo de 2026

  Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt p…

• A Tutorial on ALOS2 SAR Utilization: Dataset Preparation, Self-Supervised Pretraining, and Semantic Segmentation

  Nevrez Imamoglu, Ali Caglayan, Toru Kouyama · 17 de marzo de 2026

  Masked auto-encoders (MAE) and related approaches have shown promise for satellite imagery, but their application to synthetic aperture radar (SAR) remains limited due to challenges in semantic labeling and high noise levels. Building on our prior work with SAR-W-MixMAE, which adds SAR-specific inte…

• calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments

  Yuting Wan, Liguo Sun, Jiuwu Hao, Pin LV · 4 de marzo de 2026

  Millimeter-wave (mmWave) Radar--Camera fusion improves perception under adverse illumination and weather, but its performance is sensitive to Radar--Camera extrinsic calibration: residual misalignment biases Radar-to-image projection and degrades cross-modal aggregation for downstream 2D detection. …

• Exploring Polarimetric Properties Preservation during Reconstruction of PolSAR images using Complex-valued Convolutional Neural Networks

  Quentin Gabot, Joana Frontera-Pons, Jérémy Fix, Chengfang Ren, Jean-Philippe Ovarlez · 9 de febrero de 2026

  The inherently complex-valued nature of Polarimetric SAR data necessitates using specialized algorithms capable of directly processing complex-valued representations. However, this aspect remains underexplored in the deep learning community, with many studies opting to convert complex signals into t…