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

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 "satellitepositive" 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. INTRODUCTION Classification and detection of satellites in space imagery is important for various use cases such as safety, reconnaissance, contingency planning, space, and debris removal.