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Related Articles from SNS
T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction
arXiv:2606.05700v1 Announce Type: new Abstract: 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 with sinusoidal time encoding forecasts future latent states from K=7 acquisitions, with progressive unfreezing substantially reducing validation loss.
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
arXiv:2606.04528v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) in synthetic aperture radar imagery presents unique challenges due to severe data scarcity and SAR-specific variability. In particular, strong azimuth sensitivity in SAR induces large intra-class variation and inter-class confusion, and FSCIL sequential updates further lead to catastrophic forgetting of previously learned classes. Inspired by neural collapse, we propose an optical-guided SAR FSCIL...
Lightweight SAR Ship Detection via Contrastive Distillation
arXiv:2605.30380v1 Announce Type: new Abstract: Deep convolutional and transformer-based detectors achieve strong performance for SAR ship detection but are often computationally prohibitive for real-time or onboard deployment. Lightweight models offer improved efficiency yet struggle to capture the complex structural relationships inherent in SAR backscatter. Most existing SAR knowledge-distillation approaches rely on feature or logit matching, which enforces localized activation similarity...
Lightweight SAR Ship Detection via Contrastive Distillation
arXiv:2605.30380v2 Announce Type: replace Abstract: Deep convolutional and transformer-based detectors achieve strong performance for SAR ship detection but are often computationally prohibitive for real-time or onboard deployment. Lightweight models offer improved efficiency yet struggle to capture the complex structural relationships inherent in SAR backscatter. Most existing SAR knowledge-distillation approaches rely on feature or logit matching, which enforces localized activation...
IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal
arXiv:2606.09347v1 Announce Type: new Abstract: Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images by exploiting complementary SAR observations. Existing multimodal fusion methods typically rely on direct spatial concatenation and pixel-wise supervision, which can propagate SAR speckle noise into optical reconstruction and lead to over-smoothed results. To address these limitations, we propose...
Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images
arXiv:2606.06847v1 Announce Type: cross Abstract: Synthetic aperture radar (SAR) has become indispensable for target interpretation owing to its all-day and all-weather observation capability. In SAR target interpretation, electromagnetic scattering information provides a physically grounded cue beyond visual texture and has been widely exploited for target interpretation. However, existing methods remain dominated by local scattering center representations.
FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery
Announce Type: replace Abstract: Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applications. In recent years, although Visual Language Models (VLMs) have demonstrated strong open-world understanding capabilities on RGB images, their performance is severely limited when directly applied to the SAR field due to the complexity of the imaging mechanism, sensitivity to scattering features, and the...
Author Correction: Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant
Nature, Published online: 29 May 2026; doi:10.1038/s41586-026-10665-7Author Correction: Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant
Beyond Backscatter: InSAR coherence from detected SAR images
arXiv:2606.07374v1 Announce Type: cross Abstract: 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 backscatter magnitudes and coherence. The model is trained on 12-day SLC pairs and evaluated across different datasets, including coregistered SLC...
Chronic cocaine exposure negatively impacts Long-COVID-like outcomes produced by the SARS-CoV-2 spike protein in the rat
Acute COVID-19 outcomes are exacerbated by substance use, however, the impact of substance use on Long-COVID is unknown. Here, we investigated the impact of chronic cocaine administration on spike-induced Long-COVID-like outcomes in the rat. Rats received intermittent chronic cocaine administration and a single intravenous injection of the SARS-CoV-2 spike protein.