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.
Recommended Citation
W. Symolon and C. H. Dagli, "Single-Image Super Resolution using Convolutional Neural Network," Procedia Computer Science, vol. 185, pp. 213 - 222, Elsevier B. V., Jun 2021.
The definitive version is available at https://doi.org/10.1016/j.procs.2021.05.022
Meeting Name
Complex Adaptive Systems Conference Theme: Big Data, IoT, and AI for a Smarter Future (2021: Jun. 16-18, Malvern, PA)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
CNN; CubeSats; Super Resolution
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2021 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
18 Jun 2021
