Case-Study: Two-Phase AI Prediction for Onboard Synthetic Aperture Radar (SAR) Data Processing

SAR’s ability to operate effectively in various weather conditions makes it ideal for consistent monitoring. However, compared to other imaging methods, its inherent high data volume poses challenges for real-time AI processing. With SAR, data transmission costs to ground stations become high and providing rapid responses in time-critical scenarios with tasks such as ship detection becomes challenging. To mitigate such transmission delays, we developed a two-phase AI prediction framework from the perspective of data handling to reduce the SAR’s data volume. We explore an onboard data processing technique for SAR and show that our approach significantly reduces data volume, ensuring rapid and accurate detection. Tests on models specialized for ship detection confirm that our method maintains high detection accuracy with reduced data transmission. This showcases the potential of onboard AI computing, optimizing SAR’s advantages for time-sensitive, weather-independent applications.