Doctoral Dissertations

Keywords and Phrases

Architecture; Computer Vision; Deep Learning; Super-Resolution

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. They also 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 proposed 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 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.

Advisor(s)

Dagli, Cihan H., 1949-

Committee Member(s)

Allada, Venkata
Corns, Steven
Enke, David Lee, 1965-
Pernicka, Henry J.

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2025

Pagination

xi, 135 pages

Note about bibliography

Includes_bibliographical_references_(pages 123-131)

Rights

© 2025 William Everette Symolon , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12553

Electronic OCLC #

1545594134

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