Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Patrick B. Cunningham, PhD

Abstract

A method for characterizing unknown targets using a hyperspectral polarimetric light detection and ranging (LiDAR) system is presented. Light reflected from manmade objects tends to be more polarized than light reflected from objects in the natural world. As such, polarization measurements can be used in remote sensing applications to differentiate artificial and natural objects. Previous works have attempted to characterize objects through passive polarimetric imagery. Methods developed by Cain and Lemaster and Cunningham facilitate reconstruction of the Stokes Vector from returning light. Martin used multispectral polarimetry to classify targets when the angle of incidence (AOI) is close to 0º. Here, an algorithm is provided for classifying materials using hyperspectral LiDAR in an active remote sensing context, with AOIs ranging from -10º to 60º. A balanced accuracy of over 85% is achieved even without using AOI as an input to the classifier. Additionally, machine learning techniques for estimating target AOI are investigated.

AFIT Designator

AFIT-ENG-MS-25-M-038

Comments

An embargo was observed for this posting.

Distribution A: Approved for public release, Distribution Unlimited. PA case number 88ABW-2025-0367

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