ROMEO: Raw Onboard Multispectral Earth Observation-An Edge-AI Framework for Latency- and Bandwidth-Efficient Satellite Sensing
Keywords:
Coarse spatial coregistration, Earth observation, Edge computing, Multispectral imagery, Onboard, Raw Level-0 data, Semantic segmentationAbstract
Whether an orbital observation proves useful often depends less on what the instrument recorded than on how long the finding took to reach somebody able to act on it. Under present practice, that interval is dominated by two serial obligations, neither involving sensing: everything the instrument produces must cross a narrow channel, and a lengthy sequence of radiometric and geometric adjustments must then be completed before interpretation may begin. This study proposes ROMEO, which discharges the interpretive obligation in flight and applies it to measurements that have received no correction. The design comprises four stages: instrument interface, a minimal conditioning layer, a divided inference engine, and a controller governing what leaves the vehicle. Division of the inference engine distinguishes the proposal — a shallow admission network, the Gatekeeper, rejects tiles showing cloud, open water, or unremarkable ground, and only survivors reach a deeper segmentation network, the Mapper. Bands are brought into register by Coarse Spatial Coregistration, which replaces runtime correspondence search with constants tabulated beforehand, finishing a granule in roughly 16.63 ms on a CPU and 1.65 ms on a GPU at sub-pixel residual displacement. Supervision derives from THRawS and SegTHRawS. Because conclusions rather than imagery are released, the quantity requiring transmission falls from gigabytes to kilobytes.
References
G. Furano et al., “Towards the use of artificial intelligence on the edge in space systems: Challenges and opportunities,” in IEEE Aerospace and Electronic Systems Magazine, vol. 35, no. 12, pp. 44–56, Dec. 2020.
G. Giuffrida et al., “The Φ-Sat-1 Mission: The first on-board deep neural network demonstrator for Satellite Earth Observation,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022.
G. Meoni, R. D. Prete, F. Serva, A. De Beusscher, O. Colin and N. Longépé, “Unlocking the use of raw multispectral Earth observation imagery for onboard artificial intelligence,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 12521–12537, 2024.
G. Guerrisi, F. Del Frate and G. Schiavon, “Convolutional autoencoder algorithm for on-board image compression,” IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 151–154.
G. Guerrisi, F. D. Frate and G. Schiavon, “Artificial intelligence based on-board image compression for the Φ-Sat-2 Mission,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 8063–8075, 2023.
P. D. Stasio, A. Sebastianelli, G. Meoni and S. L. Ullo, “Early detection of volcanic eruption through artificial intelligence on board,” 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Rome, Italy, 2022, pp. 714–718.
M. P. Del Rosso, A. Sebastianelli, D. Spiller, P. P. Mathieu, and S. L. Ullo, “On-board volcanic eruption detection through CNNs and satellite multispectral imagery,” Remote Sensing, vol. 13, no. 17, Sept. 2021.
F. Massimetti, D. Coppola, M. Laiolo, S. Valade, C. Cigolini, and M. Ripepe, “Volcanic hot-spot detection using Sentinel-2: A comparison with MODIS–MIROVA thermal data series,” Remote Sensing, vol. 12, no. 5, Mar. 2020.
G. Mateo-García et al., “In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery,” Scientific Reports, vol. 13, Jun. 2023.
P. -E. Sarlin, D. DeTone, T. Malisiewicz and A. Rabinovich, “SuperGlue: Learning feature matching with graph neural networks,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 4937–4946.
P. Lindenberger, P. -E. Sarlin and M. Pollefeys, “LightGlue: Local feature matching at light speed,” 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023, pp. 17581–17592.
R. D. Prete, G. Meoni, N. Longépé, M. D. Graziano and A. Renga, “First results of vessel detection with onboard processing of Sentinel-2 raw data by deep learning,” IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 6262–6265.
