Abstract This paper presents a data driven approach to space object characterisation through the application of machine learning techniques to observational light curve data. One-dimensional convolutional neural networks are shown to be effective at classifying the shape of objects from both simulated and real light curve data. To the best of the authors’ knowledge this is the first generalised attempt to classify the shape of space objects using real observational light curve data. It is also demonstrated that transfer learning is successful in improving the overall classification accuracy on real light curve datasets. The authors develop a simulated light curve dataset using a high fidelity three-dimensional ray-tracing software. The simulator takes in a textured geometric model of a Resident Space Object as well as its ephemeris and uses ray-tracing software to generate photo-realistic images of the object that are then processed to extract the light curve. Models that are pre-trained on the simulated dataset and then fine-tuned on the real datasets are shown to outperform models purely trained on the real datasets. This result indicates that transfer learning will allow organisations to effectively utilise deep learning techniques without the requirement to build up large real light curve datasets for training.

    Highlights Develops a high-fidelity light curve simulator based on ray-tracing software. Generates a large simulated dataset to develop and test classification techniques. Develops method for space object shape classification from real light curve data. Presents shape classification results from both real and simulated datasets. Demonstrates transfer learning from simulated data improves results on real datasets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A transfer learning approach to space debris classification using observational light curve data


    Contributors:

    Published in:

    Acta Astronautica ; 181 ; 301-315


    Publication date :

    2021-01-22


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Color Photometry and Light Curve Observations of Space Debris in GEO

    Schildknecht, T. / Musci, R. / Fruh, C. et al. | British Library Conference Proceedings | 2008


    Attitude analysis of space debris using SLR and light curve data measured with single-photon detector

    Zhao, Sisi / Steindorfer, Michael / Kirchner, Georg et al. | Elsevier | 2019


    First observational campaign of space debris from O AG observatory

    Sindoni, Giampiero / Gagliarducci, Vincenzo / Di Sabato, Tommaso | IEEE | 2017



    Light space debris capturing device

    LIU XIAOQIANG / QIU YAQI / LU CHUNCHEN et al. | European Patent Office | 2023

    Free access