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Automatic Target Recognition Automatic Target Recognition Demonstration for CST ReviewDemonstration for CST Review
Professor Joseph A. OProfessor Joseph A. O’’SullivanSullivanLee Lee Montgnino Montgnino
Center for Security Technologies, Washington [email protected]://essrl.wustl.edu/~jao
Supported by: ONR, ARO, DARPAONR, ARO, DARPA
• Object Recognition and the Role of Templates• Our Methodology Based on Likelihoods• Comparative Results: MSTAR website• Open Problems:
– Fundamental Performance Bounds– Extensions to Optical Imagery
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CollaboratorsCollaborators
Michael D. DeVoreNatalia A. SchmidLee MontagninoSushil AnandAndrew LiVikas Kedia
Donald L. SnyderDaniel R. FuhrmannMichael I. MillerJeffrey H. Shapiro
Faculty Students and Post-Docs
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MotivationMotivation• Many reported approaches to ATR from SAR• Performance and database complexity are interrelated• We seek to provide a framework for comparison that:
- Allows direct comparison under identical conditions- Removes dependency on implementation details
2S12S1 T62T62 BTR 60BTR 60 D7D7 ZIL 131ZIL 131 ZSU 23/4ZSU 23/4
Publicly available SAR data from the MSTAR program
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LEE THINKS: More MotivationLEE THINKS: More Motivation• Say we have MSTAR since it is readily available to all
•Experiments are performed in a controlled manner•Data is well understood, etc.
• Extensions into Optical imaging• Direct Link (?) to Airport Security
•The Scanners implemented in SEATAC• Other Modalities
Publicly available SAR data from the MSTAR program
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Target Orientation EstimationTarget Orientation Estimation
Likelihood Model Approach:Likelihood Model Approach:ppRR||ΘΘ,,aa((rr||θθ,,aa) ) -- Conditional Data ModelConditional Data ModelppΘΘ,,aa((θθ,,aa) ) -- Prior on orientation (known or simply uniform)Prior on orientation (known or simply uniform)P(P(aa) ) -- Prior on target class (known or simply uniform)Prior on target class (known or simply uniform)
Target Target ClassifierClassifier ââ=T72=T72
Orientation Orientation EstimatorEstimator θθ=135=135
aa=T72=T72
Given a SAR image Given a SAR image rr, , determine a corresponding determine a corresponding target class target class ââ∈∈AA
Given a SAR image Given a SAR image rr and and a target class a target class ââ∈∈AA, , estimate target orientationestimate target orientation
Target Classification ProblemTarget Classification Problem
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Training and Testing Problem:Training and Testing Problem:Function Estimation and ClassificationFunction Estimation and Classification
FunctionEstimation
L(r|a,θ) Inferenceââ=T72=T72
Scene and SensorPhysics
Training Data
Raw DataProcessing
Image
• Labeled training data: target type and pose• Log-likelihood parameterized by a function:
mean image, variance image, etc.
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Function Estimation and ClassificationFunction Estimation and Classification
•• Functions are estimated fromFunctions are estimated from-- sample data sets onlysample data sets only-- physical model datasets only (PRISM, XPATCH, etc.)physical model datasets only (PRISM, XPATCH, etc.)-- combination of thesecombination of these
•• Training sets are finiteTraining sets are finite•• Computational and likelihood models have a finite Computational and likelihood models have a finite
number of parametersnumber of parameters•• Estimation errorEstimation error•• Approximation errorApproximation error•• Some regularization is neededSome regularization is needed
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ApproachApproach• Select 240 combinations of implementation parameters• Execute algorithms at each parameterization• Scatter plot the performance-complexity pairs• Determine the best achievable performance at any complexity
BMP2 Variance Image at 6 Sizes
ZIL131 Variance Image at 6 Sizes
Six different image sizes from 128x128 to 48x48
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System Parameters and ComplexitySystem Parameters and ComplexityApproximate Approximate αα((θθ,,aa) and ) and σσ22((θθ,,aa) as piecewise constant in ) as piecewise constant in θθImplementations parameterized by:Implementations parameterized by:
ww -- number of constant intervals in number of constant intervals in θθdd -- width of training intervals in width of training intervals in θθNN22 -- number of pixels in an imagenumber of pixels in an image
Database complexity ≡ log10(# floating point values / target type)
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Performance and ComplexityPerformance and ComplexityForty combinations of angular resolution and training interval width.
Variance image of aT62 tank1 Window trained over 360°
Variance images of a T72 tank72 Windows trained over 10°
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Problem StatementProblem Statement• Directly compare conditionally Gaussian, log-magnitude MSE,
and quarter power MSE ATR Algorithms- identical training and testing data- identical spatial and orientation windows
• Plot performance vs. complexity- probability of classification error- orientation estimation error- log-database size as complexity
• Use 10 class MSTAR SAR images
ApproachApproach• Select 240 combinations of implementation parameters• Execute algorithms at each parameterization• Scatter plot the performance-complexity pairs• Determine the best achievable performance at any complexity
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Approaches: Conditionally Approaches: Conditionally GaussianGaussian
J. A. OJ. A. O’’Sullivan and S. Jacobs, IEEESullivan and S. Jacobs, IEEE--AES 2000AES 2000
Model each pixel as complex Model each pixel as complex GaussianGaussian plus uncorrelated noise:plus uncorrelated noise:
( ) ( )( )( )∏ +
−
Θ +=
i
NaKr
iA
i
i
eNaK
ap 0
2
,
0, ,
1, θ
θπθrR
ˆ a Bayes r( ) = argmaxa
maxk
ˆ p rθk ,a( )ˆ θ HS r,a( ) = argmax
θ k
ˆ p rθk ,a( )
GLRT Classification and MAP Estimation:GLRT Classification and MAP Estimation:
J. A. OJ. A. O’’Sullivan, M. D. Sullivan, M. D. DeVoreDeVore, V. , V. KediaKedia, and M. Miller, IEEE, and M. Miller, IEEE--AES to appear 2000AES to appear 2000
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Approaches: LogApproaches: Log--MagnitudeMagnitudeMinimize distance between rdB = 20 log |r| and dB templates
d2 rdB,µLM( )= rdB − µLM2
Make decisions according to:
ˆ a LM r( )= argmina
mink
d2 rdB,µLM θk, a( )( )ˆ θ LM r a( )= argmin
θk
d2 rdB,µLM θk ,a( )( )
Alternatively, use a form of normalization:
d2 rdB − rdB,µLM θk,a( )− µLM θk ,a( )( )G. Owirka and L. Novak, SPIE 2230, 1994
L. Novak, et al., IEEE-AES, Jan. 1999
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Approaches: Quarter PowerApproaches: Quarter Power
d2 rQP,µQP( )= rQP − µQP
2
Minimize distance between rQP = |r|1/2 and quarter power templates
Make decisions according to:
ˆ a QP r( ) = argmina
mink
d2 rQP,µQP θk, a( )( )ˆ θ QP r,a( ) = argmin
kd2 rQP,µQP θk,a( )( )
d2 rQP
rQP,
µQP θk ,a( )µQP θk ,a( )
⎛ ⎝ ⎜ ⎞
⎠
Or, normalized by vector magnitude:
S. W. Worrell, et al., SPIE 3070, 1997Discussions with M. Bryant of Wright Laboratory
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PerformancePerformance--Complexity LegendComplexity Legend
Forty combinations of number of piecewise constant intervals and training window width
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Conditionally Conditionally GaussianGaussian ResultsResults
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Normalized Conditionally Normalized Conditionally Gaussian Gaussian ResultsResults
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LogLog--Magnitude ResultsMagnitude Results
Recognition without normalization Arithmetic mean normalized
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Quarter Power ResultsQuarter Power Results
Recognition without normalization Recognition with normalization
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SideSide--byby--Side ResultsSide ResultsComparison in terms of:• Performance achievable at a given complexity• Complexity required to achieve a given performance
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2S1 BMP 2 BRDM 2 BTR 60 BTR 70 D7 T62 T 72 ZIL131 ZSU 23 42S1 262 0 0 0 0 0 4 8 0 0 95.62%BMP 2 0 581 0 0 0 0 0 6 0 0 98.98%BRDM 2 5 3 227 1 0 14 3 5 4 1 86.31%BTR 60 1 0 0 193 0 0 0 0 0 1 98.97%BTR 70 4 5 0 0 184 0 0 3 0 0 93.88%D7 2 0 0 0 0 271 1 0 0 0 98.91%T 62 1 0 0 0 0 0 259 11 2 0 94.87%T 72 0 0 0 0 0 0 0 582 0 0 100%ZIL131 0 0 0 0 0 0 2 0 272 0 99.27%ZSU 23 4 0 0 0 0 0 2 0 1 0 271 98.91%
•• Probability of correctProbability of correctclassification: 97.2%classification: 97.2%
Target ClassificationTarget ClassificationResultsResults
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• General problem in training/testing posed as estimation/classification
• Method of sieves (polynomial splines chosen)
• Comprehensive performance-complexity study for
- Ten class MSTAR problem
- Conditionally Gaussian model
- Log-magnitude MSE
- Quarter power MSE
• Provided a framework for direct comparison of alternatives and selection of implementation parameters
• Analysis ongoing
ConclusionsConclusions
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• Extensions into other domains….
•Optical, etc.
LEE THINKS: ExtensionsLEE THINKS: Extensions