IMAGE PROCESSING FOR STRATOSPHERIC BASED SPACE SITUATIONAL AWARENESS (SSA)

This research explores the use of a stratospheric platform imager for advancing Space Situational Awareness (SSA). The primary goal was to develop and validate Resident Space Objects (RSO) detection algorithms using the RSONAR dataset, consisting of wide field-of-view imagery. RSO Detection methods were tested on 429 images, achieving F1 scores between 68% and 88%. Additionally, the potential of a dual-purpose star tracker for SSA was validated, analyzing over 27,000 images to assess astrometric and photometric properties of RSOs. Further, 544 RSO streaks were characterized based on parameters such as length, signal-to-noise ratio, and orientation. The development of RSONAR II, a next-generation camera system, allowed for capturing over 65,000 images at varying resolutions, and its optical performance was compared across two imaging systems. This study provides a comprehensive evaluation of wide field-of-view imagery for SSA and presents advancements in dual-purpose star tracker systems for future missions.