Processing of Optical Imagery Onboard Earth Observation Satellites: Benchmarking An Embedded Computing Approach

As the demand for satellite imagery increases, efficient data processing at the sensor level is essential to overcome bandwidth and latency limitations. This study investigates the use of AI-driven atmospheric cloud segmentation directly onboard satellites to optimise sensor data utilisation, reduce transmission loads, and enable autonomy in Earth observation missions. Various deep learning models were tested on embedded hardware to assess their feasibility under power and computational constraints. While some models achieved high accuracy, their resource demands made them impractical for real-time deployment. A lightweight approach, particularly using optimized versions of CloudNet, demonstrated an effective balance between efficiency and performance. The research highlights the impact of model selection and optimization techniques in enabling real-time cloud detection on small / cube satellites, offering insights into practical onboard AI implementation and providing meaningful guidance on selecting models best suited for different satellite applications based on hardware limitations.