Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks
This provides a method for fast satellite detection in space domain awareness, though it is incremental as it applies an existing CNN architecture to a specific domain.
The paper tackles the problem of detecting satellites in cluttered unresolved space imagery by using a MobileNetV2 CNN trained on a custom synthetic database, achieving validation on real telescope imagery for rapid identification.
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.