Date of Award

3-24-2016

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Kenneth W. Bauer, Jr., PhD.

Abstract

Anomaly detection in hyper-spectral imagery is a relatively recent and important research area. The shear amount of data available in a many hyper-spectral images makes the utilization of multivariate statistical methods and artificial neural networks ideal for this analysis. Using HYDICE sensor hyper-spectral images, we examine a variety of preprocessing techniques within a framework that allows for changing parameter settings and varying the methodological order of operations in order to enhance detection of anomalies within image data. By examining a variety of different options, we are able to gain significant insight into what makes anomaly detection viable for these images, as well as what impact parameter and methodology changes can have on the total classification effectiveness, false positive fraction and true positive fraction regarding classification.

AFIT Designator

AFIT-ENS-MS-16-M-119

DTIC Accession Number

Pending

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