Space Objects Classification via Light-Curve Measurements: Deep Convolutional Neural Networks and Model-based Transfer Learning

Developing a detailed understanding of the Space Object (SO) population is a fundamental goal of Space Situational Awareness (SSA). The current SO catalog includes simplified characteristic for the observed space objects, mainly the solar radiation pressure and/or drag ballistic coefficients. Such simplified description limits the dynamic propagation model used for predicting the state of motion of SO to models that assume cannon ball shapes and generic surface properties. The future SO catalog and SSA systems will have to be capable of building a detailed picture of SO characteristics. Traditional measurement sources for SO tracking, such as radar and optical, provide information on SO characteristics. These measurements have been shown to be sensitive to shape, attitude, angular velocity, and surface parameters. State-of-the-art in the literature has been advanced over the past decades and in recent years seen the development of multiple models, nonlinear state estimation, and full Bayesian inversion approaches for SO characterization. The key shortcoming of approaches in literature is their overall computational cost and the limited flexibility to deal with a larger and larger amount of data. In this paper, we present a data-driven method to classification of SO based on a deep learning approach that takes advantage of the representational power of deep neural networks. 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 a physically-based model capable of accurately representing SO reflected light as function of time, size shape and state of motion. The model generates thousands of light-curves per selected class of SO which are employ to train a deep CNN to learn the functional relationship between light curves and SO class. Additionally, a deep CNN is trained using real SO light curves to evaluate the performance on a real, but limited training set. 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. The concept of model-based transfer learning is proposed as possible path forward to increase the accuracy and speedup the training process.