{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T17:59:11Z","timestamp":1779904751861,"version":"3.53.1"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canadian Space Agency (CSA), Natural Sciences and Engineering Research Council of Canada (NSERC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A catalogue of over 22,000 objects in Earth\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22155688","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T23:49:27Z","timestamp":1659397767000},"page":"5688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor"],"prefix":"10.3390","volume":"22","author":[{"given":"Siddharth","family":"Dave","sequence":"first","affiliation":[{"name":"Department of Earth and Space Science, York University, Toronto, ON M3J 1P3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Clark","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Science, York University, Toronto, ON M3J 1P3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Regina S. K.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Science, York University, Toronto, ON M3J 1P3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","unstructured":"Jah, M. (2022, March 10). Space Surveillance, Tracking, and Information Fusion for Space Domain Awareness. NATO STO-EN-SCI-292. Available online: https:\/\/www.sto.nato.int\/publications\/STO%20Educational%20Notes\/STO-EN-SCI-292\/EN-SCI-292-02.pdf."},{"key":"ref_2","unstructured":"Oltrogge, D. (June, January 28). The \u201cWe\u201d Approach to Space Traffic Management. Proceedings of the 15th International Conference on Space Operations, Marseille, France."},{"key":"ref_3","unstructured":"(2021, March 18). 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