Abstract

This dissertation proposes algorithms for the detection of both resolved and unresolved targets in the infrared bands. Recent breakthroughs in deep learning have spurred major advancements in computer vision, but most of the attention and progress has been focused on RGB imagery from the visual band. The infrared bands such as Long Wave Infrared (LWIR), Medium Wave Infrared (MWIR), Short Wave Infrared (SWIR) and Near Infrared (NIR) each respond differently to physical phenomena, providing information that can be used to better understand the environment. The first task addressed is that of detecting vehicles in heavy clutter in MWIR imagery. A specialized network using a combination of analytically derived filters and a convolutional neural network trained using a novel objective function based on a target to clutter ratio is proposed which shows significant advantages in probability of detection and false alarm rate. The next task is that of domain adaptation where the network is deployed in a scenario different from that for which it was trained. The previously described network is adapted on the fly to improve results for new clutter data. Next, the task of hostile fire detection is considered where the unresolved image of an anti-tank guided missile launch is detected. An analysis of the relative utility of the IR bands is conducted, and data driven and parametric learning algorithms are presented which achieve a high probability of detection with a very low false alarm rate on a multi-spectral data set created by combining real IR video with radiometrically correct, synthesized missile launches at varying ranges. Finally, two methods for classifiers in the field to estimate the actual class probabilities of their environment to improve results are presented.

Notes

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Graduation Date

2022

Semester

Fall

Advisor

Mahalanobis, Abhijit

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0009383; DP0027106

URL

https://purls.library.ucf.edu/go/DP0027106

Language

English

Release Date

December 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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