Geosynchronous satellite detection and tracking with WFOV camera arrays using spatio-temporal neural networks (GEO-SPANN)

Automated monitoring of low resolution, deep-space objects in wide field of view (WFOV) imaging systems can benefit from the improved performance of deep learning object detectors. The PANDORA sensor array, located in Maui at the Air Force Maui Optical and Supercomputing Site, is an exemplar of a scalable imaging architecture that can detect dim deep-space objects while maintaining a WFOV. The PANDORA system captures 20 degrees x120 degrees images of the night sky oriented along the GEO belt at a rate of two frames per minute. Prior work has established a baseline performance for the detection of Geosynchronous Earth Orbit (GEO) satellite objects using classical, feature-based detectors. This work extends GEO object detection and tracking methodologies by implementing a spatio-temporal deep learning architecture (GEO-SPANN), further improving the state of the art in GEO satellite object detection and tracking. GEO-SPANN consists of a learned spatial detector coupled with a tracking algorithm to detect and re-identify space objects in temporal sequences. We present the detection and tracking results of GEO-SPANN on an annotated PANDORA dataset, reporting an overall maximum F1 point of 0.814, corresponding to 0.766 precision and 0.868 recall. GEO-SPANN advances strategies for autonomous detection and tracking of GEO satellites, enabling the PANDORA sensor system to be leveraged for satellite orbit catalog maintenance and anomaly detection.