High-Resolution, Slant-Angle Scene Generation and Validation of Concealed Targets in DIRSIG

179 Pages·2004·23.51 MB·English

High-Resolution, Slant-Angle Scene Generation and Validation of
Concealed Targets in DIRSIG
by
Captain Kris Barcomb, USAF
B.S. Clarkson University, 1999
M.B.A. University of La Verne, 2002
A thesis submitted in partial fulfillment of the
requirements for the degree of Master of Science
in the Chester F. Carlson Center for Imaging Science
Rochester Institute of Technology
August 24, 2004
Signature of the Author
Accepted by
Coordinator, M.S. Degree Program Date
CHESTER F. CARLSON CENTER FOR IMAGING SCIENCE
ROCHESTER INSTITUTE OF TECHNOLOGY
ROCHESTER, NEW YORK
CERTIFICATE OF APPROVAL
M.S. DEGREE THESIS
The M.S. Degree Thesis of Captain Kris Barcomb, USAF
has been examined and approved by the
thesis committee as satisfactory for the
thesis required for the
M.S. degree in Imaging Science
Dr. John Schott, Thesis Advisor
Dr. Carl Salvaggio
Scott Brown
Date
ii
THESIS RELEASE PERMISSION
ROCHESTER INSTITUTE OF TECHNOLOGY
CHESTER F. CARLSON CENTER FOR IMAGING SCIENCE
Title of Thesis:
High-Resolution, Slant-Angle Scene Generation and Validation of
Concealed Targets in DIRSIG
I,CaptainKrisBarcomb,USAF,herebygrantpermissiontoWallaceMemorial
Library of R.I.T. to reproduce my thesis in whole or in part. Any reproduction
will not be for commercial use or profit.
Signature
Date
iii
High-Resolution, Slant-Angle Scene Generation and Validation of
Concealed Targets in DIRSIG
by
Captain Kris Barcomb, USAF
Submitted to the
Chester F. Carlson Center for Imaging Science
in partial fulfillment of the requirements
for the Master of Science Degree
at the Rochester Institute of Technology
Abstract
Traditionally,syntheticimageryhasbeenconstructedtosimulateimagescapturedwith
low resolution, nadir-viewing sensors. Advances in sensor design have driven a need
to simulate scenes not only at higher resolutions but also from oblique view angles.
The primary efforts of this research include: real image capture, scene construction
and modeling, and validation of the synthetic imagery in the reflective portion of the
spectrum. High resolution imagery was collected of an area named MicroScene at the
Rochester Institute of Technology using the Chester F. Carlson Center for Imaging
Science’s MISI and WASP sensors using an oblique view angle. Three Humvees, the
primary targets, were placed in the scene under three different levels of concealment.
Following the collection, a synthetic replica of the scene was constructed and then
rendered with the Digital Imaging and Remote Sensing Image Generation (DIRSIG)
model configured to recreate the scene both spatially and spectrally based on actual
sensor characteristics. Finally, a validation of the synthetic imagery against the real
images of MicroScene was accomplished using a combination of qualitative analysis,
Gaussian maximum likelihood classification, grey-level co-occurrence matrix derived
iv
v
texture metrics, and the RX algorithm. The model was updated following each valida-
tion using a cyclical development approach. The purpose of this research is to provide
a level of confidence in the synthetic imagery produced by DIRSIG so that it can be
used to train and develop algorithms for real world concealed target detection.
Acknowledgements
I would sincerely like to thank everyone in the CIS department who made this work
possible. I would especially like to thank my committee members, Dr. John Schott,
Dr. Carl Salvaggio, and Scott Brown. They have helped guide my research throughout
the course of my time at RIT and have been an invaluable source of information. My
Air Force buddies are top notch and I’m extremely proud to serve with officers of their
caliber. Tim Hattenberger, you and Rachel have become lifelong friends of Deb and I.
We truly appreciate all that you’ve done for Lainey. I’d also like to thank my parents
and grandparents for always being there for me, teaching me right from wrong and how
to be a man. Also, thanks to my two brothers, Tim and Brandon, who would be best
friends of mine even we weren’t related. But most of all I have to thank God for all of
the blessings that have been given to me throughout my life. None of which are more
wonderful than my amazing wife Debbie and our beautiful little miracle, Lainey. None
of this would mean anything without the two of you by my side.
vi
This work is dedicated to my wife Deborah, and daddy’s little girl, Lainey.
vii
Disclaimer
The views expressed in this thesis are those of the author and do not reflect the official
policy or position of the United States Air Force, Department of Defense, or the U.S.
Government.
viii
Contents
1 Introduction 1
2 Background 7
2.1 Synthetic Image Generation . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Physical Miniature Models . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Infrared Modeling and Analysis (Irma) . . . . . . . . . . . . . . . 10
2.1.3 Camouflage Electro-Optic Simulation System (CAMEO-SIM) . . 12
2.1.4 Visual and Electro-Optical (VISEO) Detection Analysis System. 15
2.1.5 Digital Imaging and Remote Sensing Image Generation (DIRSIG) 18
2.2 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.1 Multispectral Imaging Spectrometer Instrument (MISI) . . . . . 30
2.2.2 Wildfire Airborne Sensor Program (WASP) . . . . . . . . . . . . 34
2.3 Verification and Validation . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3.1 Rank-Order Correlation (ROC) . . . . . . . . . . . . . . . . . . . 36
2.3.2 Gaussian Maximum Likelihood Classification . . . . . . . . . . . 38
2.3.3 Gray-Level Co-Occurrence Matrices (GLCM) . . . . . . . . . . . 39
2.3.4 RX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.3.5 Other Validation Efforts . . . . . . . . . . . . . . . . . . . . . . . 48
ix
CONTENTS x
2.4 Camouflage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.4.1 Camouflage Overview . . . . . . . . . . . . . . . . . . . . . . . . 54
2.4.2 Vehicle Camouflage . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.4.3 Camouflage in DIRSIG . . . . . . . . . . . . . . . . . . . . . . . 57
3 Approach 59
3.1 The Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.1.1 Collection Overview . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.1.2 Pre-Collection Phase . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.1.3 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.2 Virtual MicroScene Construction . . . . . . . . . . . . . . . . . . . . . . 71
3.2.1 Synthetic Terrain Creation . . . . . . . . . . . . . . . . . . . . . 72
3.2.2 Terrain Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.2.3 3-D Models of Manmade Objects . . . . . . . . . . . . . . . . . . 88
3.2.4 Sensor Location and Configurations . . . . . . . . . . . . . . . . 95
3.2.5 MISI Image Calibration . . . . . . . . . . . . . . . . . . . . . . . 97
3.2.6 MISI Point Spread Function . . . . . . . . . . . . . . . . . . . . . 99
3.2.7 MISI Synthetic Noise Generation . . . . . . . . . . . . . . . . . . 101
3.2.8 RX Algorithm Implementation . . . . . . . . . . . . . . . . . . . 104
4 Results 108
4.1 Qualitative Image Comparison . . . . . . . . . . . . . . . . . . . . . . . 109
4.2 Qualitative Spectral Comparison . . . . . . . . . . . . . . . . . . . . . . 114
4.2.1 Control Target Analysis . . . . . . . . . . . . . . . . . . . . . . . 114
4.2.2 Vegetation and Terrain Analysis . . . . . . . . . . . . . . . . . . 119
4.2.3 Humvee and Small Target Analysis . . . . . . . . . . . . . . . . . 121