William Symolon, a doctoral candidate in systems engineering, will defend their doctoral dissertation titled “Deep Learning Architecture Design for Nano-Satellite Image Super-Resolution.” William’s advisor, Dr. Cihan Dagli, is a professor in engineering and system engineering.

Abstract: Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. NanoSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation and research and have the potential to enhance satellite constellation resilience. However, the inherent size, weight and power limitations of NanoSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution, which limits the utility of CubeSat images for military planning purposes and national intelligence applications.

This research proposes a deep learning architecture capable of enhancing low-resolution NanoSat images to produce high-resolution imagery suitable for defense mission planning and intelligence operations. The ability to make more effective use of NanoSat technology expands the variety of imagery sources available to the Department of Defense (DoD) and contributes to improved satellite redundancy and constellation resilience.

Enhancing NanoSat imagery has potential benefit to a range of military and national intelligence missions by providing access to inexpensive, low-resolution, commercial imagery with the ability to improve the resolution for more detailed planning purposes. Military operations planners and geospatial engineers could benefit from an additional source of high-resolution imagery for determining terrain conditions, identifying potential adversary courses of action, and preparing for humanitarian assistance and disaster relief missions. Geospatial analysts would have access to an alternative intelligence source with varying revisit rates and alternative look angles to develop a more complete intelligence picture of their area of interest.