Related Experiment Video
Updated: Sep 1, 2025

Bringing the Visible Universe into Focus with Robo-AO
Published on: February 12, 2013
RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor
Siddharth Dave1, Ryan Clark1, Regina S K Lee1
1Department of Earth and Space Science, York University, Toronto, ON M3J 1P3, Canada.
Abstract:
A catalogue of over 22,000 objects in Earth's orbit is currently maintained, and that number is expected to double within the next decade. Novel data collection regimes are needed to scale our ability to detect, track, classify and characterize resident space objects in a crowded low Earth orbit. This research presents RSOnet, an image-processing framework for space domain awareness using star trackers. Star trackers are cost-effective, flight proven, and require basic image processing to be used as an attitude-determination sensor. RSOnet is designed to augment the capabilities of a star tracker by becoming an opportunistic space-surveillance sensor. Our research demonstrates that star trackers are a feasible source for RSO detections in LEO by demonstrating the performance of RSOnet on real detections from a star-tracker-like imager in space. RSOnet convolutional-neural-network model architecture, graph-based multi-object classifier and characterization results are described in this paper.
Related Concept Videos
Light Acquisition
Imaging Biological Samples with Optical Microscopy
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
X-ray Imaging
Flame Photometry: Overview
Relative Motion Analysis using Rotating Axes
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
Infrared (IR) Spectroscopy: Overview
Different compounds display unique properties due to their...

