Evaluating color fused image performance estimators

274 Pages·3.7 MB·English

by  Ogawa, James S

NPS ARCHIVE
1997.09
OGAWA,
3.
NAVAL POSTGRADUATE SCHOOL
MONTEREY, CALIFORNIA
THESIS
EVALUATING COLOR FUSED IMAGE
PERFORMANCE ESTIMATORS
by
James S. Ogawa
September, 1997
Thesis Advisor: William K. Krebs
Second Reader: James N. Eagle
Thesis
033
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1. AGENCYUSE ONLY (Leaveblank) REPORTDATE 3. REPORTTYPEANDDATES COVERED
September 1997 Master's Thesis
4 TITLEANDSUBTITLE FUNDINGNUMBERS
.
EVALUATINGCOLORFUSEDIMAGEPERFORMANCEESTIMATORS
6. AUTHOR(S) James S. Ogawa
PERFORMINGORGANIZATIONNAME(S)ANDADDRESS(ES) PERFORMING
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Naval Postgraduate School ORGANIZATION
Monterey CA 93943-5000 REPORTNUMBER
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OfficeofNavalResearch,Arlington, VA
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13. ABSTRACT (maximum 200words) — —
Thisthesisevaluatedtheeffectivenessofsensorfusion combininginfraredandlow-light-levelimagery toimprovetheF/A-18
targetstandoffrangerequirement. Severalhumanperformancestudieshaveshowninconsistentresultsregardingthebenefitsofcolor-
fusedimagery. Onemethodtotestthevalidityofsensorfusionistousemathematicalmodelsthatsimulateandpredictthedetection
abilitiesofthehumanvisual system. Themathematicalmodelsarederivedquantitativelyfromtheimagestatistics,whilethebehavior
dataareaqualitativemeasureofahumanobserver. Thisthesisdevelopedastatisticalanalysistocompareandcontrastthese
techniquestoassess sensorfusion. Thefourmodelsevaluatedwere: aGlobalmatchedfilters,aLocalmatchedfilter,aTemplate
matchingfilter,andacontrast-baseimagequalitymetric. Ofthefourmodels,theGlobalmatchedfilterproducedthehighestdegreeof
correlationwiththehumandata. TheGlobalmatchedfiltermoderatelypredictedwhichofthe single-bandsensorsandwhichofthe
fused sensorsprovidedthehighersensitivitiesdespitethecharacteristicallydifferentscenes. Althoughtherearemanyrefinementsthat
needtobe explored, theGlobalmatchedfilterconceptmaybeusedtoevaluateandcomparethemanydifferentfusionalgorithms
beingproposed.
14. SUBJECTTERMS 15.NUMBEROFPAGES
DigitalImageProcessing, SensorFusion,MatchedFilters,ImageQuality,ImageQualityMetrics, 262
Templatematching,TargetDetection,Receiver-OperatorCurves, ContrastSensitivityFunction,
CensoredRegression.
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TIONOFREPORT TIONOFTHISPAGE TIONOFABSTRACT ABSTRACT
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EVALUATING COLOR FUSED IMAGE PERFORMANCE ESTIMATORS
James S. Ogawa
Lieutenant, United States Navy
B.S., United States Naval Academy, 1990
Submitted in partial fulfillment
ofthe requirements for the degree of
MASTER OF SCIENCE IN OPERATIONS RESEARCH
from the
NAVAL POSTGRADUATE SCHOOL
September 1997

ffpoSK
SCHOOL
MONTEREY CA 93943-5101
ABSTRACT
—
This thesis evaluated the effectiveness ofsensor fusion combining infrared and low-
—
light-level imagery to improve the F/A-18 target standoffrange requirement. Several
human performance studies have shown inconsistent results regarding the benefits of
color-fused imagery. One method to test the validity ofsensor fusion is to use
mathematical models that simulate and predict the detection abilities ofthe human visual
system. The mathematical models are derived quantitatively from the image statistics,
while the behavior data are a qualitative measure ofa human observer. This thesis
developed a statistical analysis to compare and contrast these techniques to assess sensor
fusion. The four models evaluated were: a Global matched filters, a Local matched filter,
a Template matching filter, and a contrast-base image quality metric. Ofthe four models,
the Global matched filter produced the highest degree ofcorrelation with the human data.
The Global matched filter moderately predicted which ofthe single-band sensors and
which ofthe fused sensors provided the higher sensitivities despite the characteristically
different scenes. Although there are many refinements that need to be explored, the
Global matched filter concept may be used to evaluate and compare the many different
fusion algorithms being proposed.
VI
TABLE OF CONTENTS
INTRODUCTION
I. 1
BACKGROUND
II. 5
FUSED COLORENHANCEMENT
A. 5
B. MATCHED FILTERPERFORMANCE ESTIMATION 8
HUMAN FACTORS STUDY TO INVESTIGATE
C.
FUSED COLOR IMAGES
12
D. IMAGE QUALITY METRICS (IQM'S) 13
E. SENSITIVITY (d') IN SEARCH
DETECTION THEORY
15
F. RESPONSE BIAS (p) IN SEARCH
DETECTION THEORY
19
G. HYPOTHESES 20
MODEL
III. 23
A. THEORETICAL DEVELOPMENT OF A
MATCHED FILTER 23
B THEORETICAL DEVELOPMENT OF THE
.
CONTRAST-BASED IMAGE QUALITY METRIC 28
SETUP OF PLANNED COMPARISONS BASED
C.
UPON THE HYPOTHESES 29
D. MODELING ANORMAL DISTRIBUTION
FOR CENSORED REGRESSION 30
METHODS
IV. 35
A. MATCHED FILTER PROGRAM DEVELOPMENT 35
B. BACKGROUND AND TARGET IMAGERY 36
C. FILTERING AN IMAGE WITH AMATCHED FILTER 38
vii
D. INTERPRETING MATCHED FILTER OUTPUT VALUES 43
E. DEVELOPMENT OF EMPIRICAL RECEIVER-
OPERATOR CURVES
46
DETERMINATION OF THE HUMAN VISUAL
F.
SYSTEM SENSITIVITY AND BIAS 48
G. DETERMINATION OF MATCHED FILTER
SENSITIVITY AND BIAS 50
H. OBTAINING THE CSF BASED IQM OUTPUT VALUES 52
SETUP OF PLANNED COMPARISONS BASED
I.
UPON THE PROPOSED HYPOTHESES
53
TREND ANALYSIS OF THE MODELED RESULTS
J.
USING CENSORED REGRESSION 54
V. RESULTS 57
A. ANOVA/PLANNED COMPARISON ANALYSIS 66
B. BONFERRONI TESTS FOR COMPARISONS 69
C. TREND ANALYSIS WITH CENSORED DATA 71
VI. CONCLUSIONS 75
APPENDIX A MATCHED FILTER MAIN PROGRAM (MATLAB) 81
APPENDIX B M-FILE TO LOAD IMAGES AND TARGETS 85
APPENDIX C M-FILE TO INPUT FILTERING OPTIONS 93
APPENDIX D M-FILE TO PREPROCESS IMAGES AND/OR TARGETS 99
APPENDIX E M-FILE TO DEVELOP MATCHED FILTER COEFFICIENTS 03
1
APPENDIX F M-FILE TO DEVELOP A TEMPLATE MATCHING FILTER Ill
Vlll