Radiometrically Accurate Thermal Vehicle Targets for Synthetic Video Development

123 Pages·2016·2.75 MB·English

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David B. Rhodes
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Rhodes, David B., "Radiometrically Accurate Thermal Vehicle Targets for Synthetic Video Development"
(2016). Thesis. Rochester Institute of Technology. Accessed from
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Radiometrically Accurate Thermal Vehicle Targets for Synthetic
Video Development
by
David B. Rhodes
A.S. Corning Community College, 2004
B.S. SUNY Brockport, 2006
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
of the College of Science
Rochester Institute of Technology
December 9, 2016
Signature of the Author
Accepted by
Coordinator, M.S. Degree Program Date
CHESTER F. CARLSON CENTER FOR IMAGING SCIENCE
ROCHESTER INSTITUTE OF TECHNOLOGY
COLLEGE OF SCIENCE
ROCHESTER, NEW YORK
CERTIFICATE OF APPROVAL
M.S. DEGREE THESIS
The M.S. Degree Thesis of David B. Rhodes
has been examined and approved by the
thesis committee as satisfactory for the
thesis required for the
M.S. degree in Imaging Science
Dr. Zoran Ninkov, Thesis Advisor
Dr. J. Daniel Newman
Dr. David Messinger
Date
ii
Radiometrically Accurate Thermal Vehicle Targets for Synthetic
Video Development
by
David B. Rhodes
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
Collecting large scientific quality thermal infrared image and video data sets is an expen-
sivetimeconsumingendeavor. Thermalinfraredimagerscostmuchmorethancomparable
visible systems and require skilled experienced operators. Also, time and experienced per-
sonnel are required to collect quality ground truth. Often it is advantageous to perform
computersimulationsasanalternativetocollectingimageandvideodatawithrealcamera
systems. Aslongasenoughphysicsisincorporatedintothemodelstogiveaccuratelycom-
parable results to real imagery, simulated data can be used interchangeably. Generating
synthetic images and video has the added benefit of being flexible as the user has control
over every aspect of the simulation. Simulations are not subject to restrictions such as
location,weatherconditions,timeofday,ortimeofyear. Groundtruthisassignedinstead
of measured in the synthetic world so it is known a priori. This thesis illustrates a method
of using the Digital Image and Remote Sensing Image Generation (DIRSIG) software to
create simulated infrared images and video of validated thermal target vehicle models in-
side thermal infrared wide-area scenes. A finite difference heat propagation and surface
TM
temperature solver, ThermoAnalytics Multi-Service Electro-optic Signature (MuSES ),
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was used to accurately model the emissive thermal target vehicles. Validation of the ther-
mal target vehicle model was performed using images taken from a laboratory calibrated
MWIR camera. Images taken with the calibrated camera of the same type of vehicle as
the target model were compared to the synthetic images for the same conditions for vali-
dation. Target vehicle motion was added to the simulations through the use of Simulation
of Urban Mobility (SUMO), DIRSIGs movement files, and custom python scripting. The
output images from DIRSIG were then laced together into video. The resulting video was
used to test three tracking algorithms illuminating each one’s strengths and weaknesses.
Acknowledgements
Iwouldliketothankmythesiscommitteemembers: Dr. ZoranNinkov,Dr. DanNewman,
and Dr. Dave Messinger. My research has been guided by your support and guidance.
Thank you Zoran for getting me involved with DIRSIG while I was still at U of R. Thanks
to CEIS and ITT (now Harris Corporation) for funding my research. Without funding I
would not have been able to finish. Thank you Dan Newman, Paul Lee, and Greg Gosian
for offering suggestions on how my research should proceed from a programmatic point of
view. Thank you Ross Robinson, Kenny Fourspring, and Kyle Ausfeld for being in the lab
to bounce questions off and helping with my numerous data collections. Thanks you Judy
Pipher and Craig McMurtry from U of R for helping me to understand infrared detector
arraysandofferingyourcamerasystemandtruckfordatacollections. ThankyouRolando
Raquen˜o, Andy Scott, Scott Brown, Adam Goodenough, Niek Sanders, Mike Gartley, and
Dave Pogorzala for answering my many DIRSIG questions. Thank you Mike Presnar for
helping me get a good start on making video in DIRSIG. Thank you to Jason Faulring
for helping me with the digital interface to the KIR-310 camera and operating WASP and
WASP Lite on data collections. Thank you Nina Raquen˜o for loaning me temperature
measurement equipment and measuring materials for me. Thank you Dr. Schott for
teachingmeaboutremotesensingandfordiggingintothevaultforoldTHERMvalidation
documentation that I’m sure do not exist anywhere else. Thank you Brett Matzke, Jared
Herweg, and Kenny Fourspring for keeping me sane. The many bike rides and times we
hung out kept me from imploding under the stress of being a single parent and a full-time
graduate student. Thank you Diane Alsup for proof reading this thesis and spending your
life with me. You are all very much appreciated.
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This thesis is dedicated to my four children, Patrick, Anthony, Debbie, and Kylie. Your
love and support has kept me going these many years. I am very proud of the fine people
you have grown to become.
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Curriculum Vitae
The author was born on September 23, 1974 in Elmira, NY to Gerald and Regina Rhodes.
He attended Corning Community College part-time from 2000 to 2004, and graduated
highest program GPA with an Associates in Science. He attended SUNY Brockport from
2004to2006graduatingCumLaudewithaBachelorofSciencedualdegreeinMathematics
and Physics. He attended University of Rochester from 2006 to 2009 completing the
Physics Coursework before leaving to attend RIT from 2009 to 2012. The author from
2012 to the time of the publishing of this thesis is employed as a Senior Image Scientist
working for Harris Corporation, Rochester NY.
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Contents
Acknowledgements v
Dedication vi
Curriculum Vitae vii
Table of Contents x
List of Figures xiv
List of Tables xiv
List of Symbols xv
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Previous Work and Existing Methods . . . . . . . . . . . . . . . . . . . . . 3
1.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Background Information 10
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
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CONTENTS ix
2.2 Radiometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 Radiometric Quantities . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Blackbody Radiation and Emissivity . . . . . . . . . . . . . . . . . . 13
2.3 Atmospheric Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Heat Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Software 20
3.1 DIRSIG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.2 Scene Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.3 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.4 Emissivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1.5 Extinction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1.6 Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.7 Platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.8 Platform Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1.9 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1.10 DIRSIG’s Radiometry Solvers . . . . . . . . . . . . . . . . . . . . . . 30
3.1.11 DIRSIG’s Thermal Solver (THERM) . . . . . . . . . . . . . . . . . . 30
TM
3.1.12 External Thermal Model (ThermoAnalytics MuSES ) . . . . . . . 31
3.2 MODTRAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
TM
3.3 MuSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4 SUMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33