Researchers found that satellites running AI onboard don't need the cleaned-up imagery ground stations usually produce - but what they lose depends on how the image gets worse, not how much.
A new study tested three lightweight object detectors - YOLOv5s, YOLOX-S, and NanoDet - on satellite images deliberately degraded in three ways: lower signal-to-noise ratio, blurrier optics (measured via modulation transfer function), and coarser ground sampling distance, which is how much real-world area each pixel covers. Using Maxar's very-high-resolution imagery as a baseline, researchers simulated the kind of raw, unprocessed feed a satellite's own onboard computer would see, rather than the polished images normally downlinked to Earth. Vessel-detection accuracy didn't fall off a cliff as quality dropped - it depended heavily on which specific degradation was involved. Coarser resolution hurt consistently across the board, while blur and noise mattered more or less depending on the detector and starting resolution, with the worst results showing up when blur and noise combined.
This is a real engineering trade-off for the growing fleet of satellites doing wildfire, vessel, and cloud detection onboard instead of waiting to beam raw data down for processing - bandwidth and power are scarce, so every choice about sensor quality versus onboard compute cost matters. The findings suggest engineers can cut corners on optics or compression in some cases without wrecking detection performance, as long as they protect resolution first.
It's a useful corrective to vague claims that AI can simply compensate for worse sensor data: compensate compared to what, and compensate for how much degradation, turn out to be the real questions.