This work presents a data-driven method for the classification of light curve measurements of Space Objects (SOs) based on a deep learning approach. Here, we design, train, and validate a Convolutional Neural Network (CNN) capable of learning to classify SOs from collected light-curve measurements. The proposed methodology relies on a physics-based model capable of accurately representing SO reflected light as a function of time, size, shape, and state of motion. The model generates thousands of light-curves per selected class of SO, which are employed to train a deep CNN to learn the functional relationship. between light-curves and SO classes. Additionally, a deep CNN is trained using real SO light-curves to evaluate the performance on real data, but limited training set. The CNNs are compared with more conventional machine learning techniques (bagged trees, support vector machines) and are shown to outperform such methods, especially when trained on real data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Space Objects Classification via Light-Curve Measurements Using Deep Convolutional Neural Networks


    Weitere Titelangaben:

    J Astronaut Sci


    Beteiligte:
    Linares, Richard (Autor:in) / Furfaro, Roberto (Autor:in) / Reddy, Vishnu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2020-09-01


    Format / Umfang :

    29 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Automated Truck Taxonomy Classification Using Deep Convolutional Neural Networks

    Almutairi, Abdullah / He, Pan / Rangarajan, Anand et al. | Springer Verlag | 2022


    Covert photo classification by deep convolutional neural networks

    Zuo, H. / Lang, H. / Blasch, E. et al. | British Library Online Contents | 2017


    Grasping Unknown Objects Using Convolutional Neural Networks

    Krishna Prasad, Pranav / Stähle, Benjamin / Chernov, Igor et al. | BASE | 2020

    Freier Zugriff