Date of Award:

8-2026

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Mathematics and Statistics

Committee Chair(s)

Kevin R. Moon

Committee

Kevin R. Moon

Committee

Brennan L. Bean

Committee

Alan Wisler

Committee

Daniel Coster

Committee

Zigfried Hampel-Arias

Abstract

Each pixel from a hyperspectral camera measures the intensity of light over a continuous range of wavelengths, which is in contrast to traditional color cameras, which just measure the intensity of red, green, and blue wavelengths of light. Longwave infrared hyperspectral images can be used to detect gases from a distance by measuring how different materials emit and absorb heat. This makes them useful for applications such as monitoring industrial emissions or locating hazardous gas leaks. In practice, however, gas signatures in these hyperspectral images are often weak and easily obscured by variations in the background scene, making reliable identification difficult. This dissertation explores how modern machine learning methods can improve the analysis of gas plumes in longwave infrared hyperspectral imagery. First, we develop techniques to better estimate the background signal beneath a gas plume by leveraging information from nearby regions in an image. These improved background estimates make it easier for our neural network to correctly identify gases, especially when the signal is faint. Second, we investigate how multiple hyperspectral images of the same scene can be combined to build a 3D representation of the environment using neural networks, specifically, with a model called a neural radiance field. These reconstructions capture both the geometry of the scene and its infrared spectral properties, providing new ways to analyze a gas plume. Together, these contributions show that combining spatial information, multi-view imaging, and machine learning leads to more reliable remote sensing and analysis of gas plumes. This work lays the groundwork for future systems that can better detect, identify, and quantify gases in real-world environments.

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