Unsupervised learning for target tracking and background subtraction in satellite imagery

Jonathan S. Kent; Charles C. Wamsley; Davin Flateau; Amber Ferguson

doi:10.1117/12.2580620

12 April 2021 Unsupervised learning for target tracking and background subtraction in satellite imagery

Jonathan S. Kent, Charles C. Wamsley, Davin Flateau, Amber Ferguson

Author Affiliations +

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117460H (2021) https://doi.org/10.1117/12.2580620
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper describes an unsupervised machine learning methodology capable of target tracking and background suppression via a novel dual-model approach. “Jekyll” produces a video bit-mask describing an estimate of the locations of moving objects, and “Hyde” outputs a pseudo-background frame to subtract from the original input image sequence. These models were trained with a custom-modified version of Cross Entropy Loss. Simulated data were used to compare the performance of Jekyll and Hyde against a more traditional supervised Machine Learning approach. The results from these comparisons show that the unsupervised methods developed are competitive in output quality with supervised techniques, without the associated cost of acquiring labeled training data.

Conference Presentation

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Jonathan S. Kent, Charles C. Wamsley, Davin Flateau, and Amber Ferguson "Unsupervised learning for target tracking and background subtraction in satellite imagery", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117460H (12 April 2021); https://doi.org/10.1117/12.2580620

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