T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction
arXiv:2606.05700v1 Announce Type: cross 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. The model operates on amplitude alone; InSAR coherence serves exclusively as independent pseudo-gr
The continuous advancements in AI, particularly self-supervised learning and transformer architectures, are making sophisticated temporal anomaly detection in complex datasets like SAR amplitude stacks increasingly feasible.
This technology enables proactive monitoring and rapid identification of subtle changes in environments, which can have significant implications for strategic intelligence, infrastructure assessment, and resource management.
The ability to detect temporal anomalies in SAR data without human supervision, using latent prediction, fundamentally alters the speed and scale at which large geographical areas can be surveilled and analysed.
- · Defence/Intelligence Agencies
- · Satellite Imagery Providers
- · AI/ML companies specializing in geospatial analysis
- · Environmental Monitoring Organizations
- · Traditional manual image analysis workflows
- · Competitors with less advanced AI integration for SAR data
Automated detection of irregular activities or environmental shifts becomes more widespread and efficient.
Improved predictive capabilities for potential conflicts, natural disasters, or resource exploitation drive more timely interventions.
The enhanced data analysis capabilities could lead to new doctrines in national security and resource governance, increasing both transparency and surveillance potential globally.
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Read at arXiv cs.LG