Real-time space object tracklet extraction from telescope survey images with machine learning

Real-time space object tracklet extraction from telescope survey images with machine learning

In this study, a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions. As in all machine learning (ML) applications, a series of steps is required for a working pipeline: dataset creation, preprocess...

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Bibliographic Details
Main Authors: Cipollone, R. (Author), De Vittori, A. (Author), Di Lizia, P. (Author), Massari, M. (Author)
Format: Article
Language:English
Published: Tsinghua University 2022
Subjects:
Deep neural networks
Image segmentation
Machine learning
machine learning (ML)
Pipeline processing systems
Pipelines
Post-processing
Real images
Real- time
space debris
Space debris
Space objects
Space surveillance
Space surveillance and tracking
space surveillance and tracking (SST)
Statistical tests
Telescope image
telescope images
Telescopes
Time-space
tracklet
Tracklets
U-net
U-Net
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