Raw Imagery Impacting Your AI: Should You Care?
Onboard AI is gaining interest for space applications such as vessel, wildfire, and cloud detection, where real-time processing can improve mission reactivity and reduce downlink needs. However, onboard models may operate on raw or minimally processed imagery rather than on restored ground products. This study evaluates how image degradation affects object detection by varying Signal-to-Noise Ratio (SNR), Modulation Transfer Function (MTF) at Nyquist, and Ground Sampling Distance (GSD). Controlled degradations are applied to Very High Resolution Maxar imagery, and three lightweight detectors, YOLOv5s, YOLOX-S, and NanoDet, are evaluated on the resulting operating points. The results show ...
| Authors | Adrien Dorise, Marjorie Bellizzi, Stéphane May |
|---|---|
| Primary category | eess.IV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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