Date of Award
3-2023
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Stephen C. Cain, PhD
Abstract
MSI and HSI techniques allow users to determine the material composition of an object at range. To avoid labor-intensive manual classification, ML is used to determine the most likely material contained in a given pixel of a target image. Previous work primarily focuses on terrestrial applications; this paper extends these techniques into the low-illumination space situational awareness domain, which is of critical importance to national security. HSI datacubes are preprocessed with RL deconvolution as a means of reducing the effects of the optical PSF; then, statistical ML techniques, including k-NN, LDA, QDA, and SVMs are implemented as means of assigning material class membership to the resulting regions.
AFIT Designator
AFIT-ENG-MS-23-M-058
DTIC Accession Number
AD1340951
Recommended Citation
Stumpf, Stephen M., "Spectral Material Classification of Orbital Objects - Applying machine learning to visible and near-infrared spectral scenes" (2023). Theses and Dissertations. 6939.
https://scholar.afit.edu/etd/6939
Comments
An embargo was observer for posting this thesis on AFIT Scholar.
Approved for public release. PA case number 88ABW-2023-0172