Yasin Zamani, University of Utah; Joel Amert, NASA/MSFC; Thomas Bryan, NASA/MSFC; Neda Nategh, University of Utah
Keywords: orbital debris, image processing, object detection, classification algorithms, orbital debris tracking systems, optical camera
Abstract:
This study develops a vision-based detection and classification algorithm to address the challenges of in-situ small orbital debris environment classification including debris observability and instrument requirements for small debris observation. The algorithm operates in near real time and is robust under difficult tasks in moving objects classification such as multiple moving objects, objects with various movement trajectories and speeds, very small or faint objects, and substantial background motion. The performance of the algorithm is optimized and validated using space image data available through simulated environments generated using NASA Marshall Space Flight Centers Dynamic Star Field Simulator of on-board optical sensors and cameras.
Date of Conference: September 17-20, 2019
Track: Orbital Debris