Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks

2022/07/23 by Jarred Jordan, Jordan, Jarred, Daniel Posada +9
Engineering · Physics and Astronomy · #Astro and Planetary Science #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Space Satellite Systems and Control #Spacecraft Design and Technology

paper · pdf · doi:10.48550/arxiv.2207.11412

openalex publication_date 2022/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast, mobile detection of satellites, and rejection of stars, in cluttered unresolved space imagery. First, a custom database is created using imagery from a synthetic satellite image program and labeled with bounding boxes over satellites for "satellite-positive" images. The CNN is then trained on this database and the inference is validated by checking the accuracy of the model on an external dataset constructed of real telescope imagery. In doing so, the trained CNN provides a method of rapid satellite identification for subsequent utilization in ground-based orbit estimation.

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