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Unsupervised Learning for Target Tracking and Background Subtraction in Satellite Imagery

Simulated data used to compare the performance of Jekyll and Hyde against a more traditional supervised Machine Learning approach.

Data and Resources

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Cite this as

Jonathan S. Kenta, Charles C. Wamsley, Davin Flateau, Amber Fergusson (2024). Dataset: Unsupervised Learning for Target Tracking and Background Subtraction in Satellite Imagery. https://doi.org/10.57702/hvvezho5

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It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.1117/12.2580620
Author	Jonathan S. Kenta
More Authors	Charles C. Wamsley Davin Flateau Amber Fergusson