Date of Award
3-2023
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Electrical and Computer Engineering
First Advisor
Kenneth M. Hopkinson, PhD
Abstract
The field of remote sensing continues to expand in both commercial and defense domains. Development of advanced space based EOIR sensors has driven corresponding demand for sensor data for algorithm development. The AFIT Sensor and Scene Emulation Tool (ASSET) produces realistic synthetic electro-optical and infrared (EO/IR) data with absolute truth for the purpose of clutter suppression, target detection, and tracking algorithm development. This thesis presents a novel model which transforms panchromatic images into realistic hyperspectral reflectance images. The direct application of this model is to allows users to generate hyperspectral background images as inputs to ASSET allowing users to benefit from improved radiometric accuracy without prior expert knowledge of sensor inputs.
AFIT Designator
AFIT-ENG-MS-23-M-067
Recommended Citation
Wagner, Bret M., "Entering Hyperspace: Conditional Hyperspectral Reflectance Image Generation using Convolutional Neural Networks" (2023). Theses and Dissertations. 6942.
https://scholar.afit.edu/etd/6942
Comments
Approved for public release. Case number on file.
A 12-month embargo was observed.