Lee, Regina S. K.Kunalakantha, Perushan2024-11-072024-11-072024-08-022024-11-07https://hdl.handle.net/10315/42528This research presents several contributions aimed at improving the current state of Space Situational Awareness (SSA) using optical imagery. The first component involved the development of an image capture algorithm which was successfully used to acquire over 90000 optical images from the stratosphere, with hundreds of visually verified Resident Space Objects (RSOs). The second component involved the development of a novel RSO tracking algorithm which was able to detect 87% of the RSOs in an 878-image dataset at least once. The third component proposed an automated annotation framework and corresponding four-tool annotation suite to develop a 500-image dataset to train and test another RSO tracking algorithm. The final component involved the demonstration of a dual-purpose payload, performing RSO detection alongside an existing Attitude Determination (AD) algorithm which detected 11 unique RSOs in real-time during another stratospheric balloon mission.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Aerospace engineeringAtmospheric sciencesAstronomyElectronic Thesis or Dissertation2024-11-07SpaceResident space objectsMachine learningICPAlgorithmSpace objectsObject trackingRSORSO trackingRSO dataData labellingSpace mission