Cloud Filtering and Novelty Detection using Onboard Machine Learning for the EO-1 Spacecraft

We deployed three new data analysis algorithms onboard the Earth Observing 1 (EO-1) spacecraft and evaluated their performance over a five-month period. The algorithms include two cloud detectors and an unsupervised novelty detector. Together they provide the first demonstration of ensemble, Bayesian, and novelty detection methods onboard EO-1. Onboard performance on a diverse collection of targets was similar to or better than that observed in ground testing. These algorithms can be used to benefit future missions by aiding onboard decisions about data prioritization to optimize the use of limited downlink as well as potentially to enable autonomous response actions.

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