Photometric space object classification via deep learning algorithms

doi:10.1016/j.actaastro.2021.05.008

2021-08-01

PRE

Tong Liu

Ulrich Schreiber *

DOI：10.1016/j.actaastro.2021.05.008

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摘要

En 中文

Accurate time transfer by time of flight measurements via diffuse reflections on passive orbiting space debris targets requires a selection of suitable objects out of a large catalogue of debris items. In this paper, we report on our development of an automatic classification system of space objects based on photometric observations of sun illuminated satellite and debris items from the Mini-Mega TORTORA (MMT) system observation data base by a deep learning algorithm. A deep neural network model based on a convolutional long short-term memory network has been designed to identify four different object categories with a test accuracy of over 85%. The method is also suitable for an automated analysis of the temporal evolution of the orbit motion of specific space objects.

Keyword:

Space objects classification

Photometry

Deep neural network

Convolutional long short-term memory network