Machine Learning
Building a Maritime Anomaly Detection System with SAR Imagery
How I combined synthetic data generation, machine learning, and edge computing to detect suspicious vessel behavior
November 15, 2024
12 min read
SAR ImagingSynthetic DataEdge AIPythonComputer Vision
A technical walkthrough of my honors thesis project that tackles the challenge of monitoring vast ocean regions using Synthetic Aperture Radar and machine learning.
The Challenge
Traditional maritime surveillance methods struggle with scale, weather conditions, and data transmission limitations. This project explores how SAR imagery combined with synthetic data can revolutionize anomaly detection...
SAR (Synthetic Aperture Radar) works in all weather conditions and doesn't require sunlight, making it ideal for 24/7 maritime monitoring.
Technical Architecture
# Example: Generating synthetic vessel trajectories\ndef generate_anomaly_trajectory(base_route, anomaly_type):\n if anomaly_type == 'speed_change':\n return inject_speed_anomaly(base_route)\n elif anomaly_type == 'off_route':\n return inject_deviation(base_route)Key Takeaways
- •Synthetic data generation can effectively train anomaly detection models when real-world labeled data is scarce
- •Edge deployment requires careful optimization of model size and inference speed
- •Multi-frame temporal analysis significantly improves detection accuracy over single-frame methods