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

• URL: http://arxiv.org/abs/2207.11412v1
• Date: Sat, 23 Jul 2022 04:28:45 GMT
• Title: Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks
• Authors: Jarred Jordan, Daniel Posada, David Zuehlke, Angelica Radulovic, Aryslan Malik, and Troy Henderson
• Abstract summary: This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast, mobile detection of satellites. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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