Abstract
Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. CubeSats 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 CubeSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution. Low resolution limits the utility of CubeSat images for military planning purposes and national intelligence applications. This paper implements a super-resolution deep learning architecture and proposes potential applications to CubeSat imagery.
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