Date of Award
3-22-2012
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Kenneth W. Bauer, PhD.
Abstract
Anomaly detection has been used successfully on hyperspectral images for over a decade. However, there is an ever increasing need for real-time anomaly detectors. Historically, anomaly detection methods have focused on analysis after the entire image has been collected. As useful as post-collection anomaly detection is, there is a great advantage to detecting an anomaly as it is being collected. This research is focused on speeding up the process of detection for a pre-existing method, Linear RX, which is a variation on the traditional Reed-Xiaoli detector. By speeding up the process of detection, it is possible to create a real-time anomaly detector. The window covariance matrix is our main area focus for speed improvement. Several methods were investigated, including QR factorization and tracking the change in the window covariance matrix as it moves through the image. Finally, performance comparisons are made to the original Linear RX detector.
AFIT Designator
AFIT-OR-MS-ENS-12-04
DTIC Accession Number
ADA558575
Recommended Citation
Bush, Kelly R., "Using QR Factorization for Real-Time Anomaly Detection of Hyperspectral Images" (2012). Theses and Dissertations. 1200.
https://scholar.afit.edu/etd/1200