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AAccurate and Robust Automatic Target RecognitionAutomatic Target Recognition Method for SAR Imagery withMethod for SAR Imagery with
SOM-based ClassificationSOM based ClassificationShouhei Kidera and Tetsuo Kirimoto
University of Electro-Communications, Tokyo, JAPANInternational Symposium on Remote Sensing (ISRS) 2012 and y p g ( )
International Conference on Space, Aeronautics and Navigation Electronics (ICASANE) 2012, Incheon, Korea, 11th, Oct., 2012
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IntroductionMicrowave radar : Applicable to adverse weather or darkness
SAR(Synthetic aperture radar) : High-resolution imaging methodSAR(Synthetic aperture radar) : High resolution imaging method
Target recognition with SAR imagery : A great deal of experience is required because SAR image is definitely different from optical image ⇒ ATR(Automatic Target Recognition) method is in demand
Optical image SAR image
SAR
Optical image SAR image
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Conventional ATR TechniquesConventional ATR Techniques
Neural Network based approachTraditional ATR methods for SAR imagery
Neural Network based approach ・[1] C. M. Pilcher et al., IEEE Trans. Aerosp. Electron. Syst., 2011⇒ Classification employing range profile dataClassification employing range profile data
・[2] M. Martorella, et al., IEEE Trans. Aerosp. Electron. Syst., 2011⇒ Exploiting ISAR images with full polarimetric datap g g p
Other ATR approach ・[3] Q. Zhao, IEEE Trans. Aerosp. Electron. Syst., 2001⇒ SVM (Support Vector Machine) based classification
P bl i t diti l th dInaccurate classification in the case of
strong noisy situations or observation angle errors
Problem in traditional methods
strong noisy situations or observation angle errors⇒ More robust ATR method is proposed here !
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System Model・Mono-static radar system・Targets with arbitrary shapes g y p・Transmitted signal : Frequency sweeping (complex value)・SAR image generation : Back projection algorithm ・Binarization method: Otsu’s discriminant analysis method
SAR image
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System Model・Mono-static radar system・Targets with arbitrary shapes g y p・Transmitted signal : Frequency sweeping (complex value)・SAR image generation : Back projection algorithm
SAR imageSAR image Binarized image
・Binarization method: Otsu’s discriminant analysis method
Binarization
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Conventional Method(Neural Network based Learning)
l k d l 3 l ( idd O )Neural network model : 3 layer (Input, Hidden, Output) [ ]
1,,1 Nxx L=x : Binarized SAR image sequence
)1(,)( == mxmu jj
[ ]1
Neuron model of each layer 3 layer neural network
)()(
1)(
)(,)(
1m
⎟⎞
⎜⎛
=
∑−
muNj
jj
)3,2(,
)1()(exp1!
,
=
⎟⎟⎠
⎞⎜⎜⎝
⎛−−+ ∑
=
m
mumwi
iji
)3,2(, m
)(mu j : Value of j th neuron
)(, mw ji : Weight from i th to j th neuron
Training method: Back propagation algorithm
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Conventional Method(Neural Network based Classifier)
Classification Principle : Assessing difference among neuron’s values of output layer
Optimal class number is determined :
)3(
)3()3(minarg
tr
tr
1
NNopt
k
NkK
u
uu −=
≤≤ )3(1 tr kNk u≤≤
: Output of training data)3(trku
)3(u : Output of test data
Problem : Inaccuracy in lower SNR situations or other observation errors (e.g. Angle of nose)
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Proposed ATR Method(SOM Training phase)
SOM (S lf O i i M ) U i d l t k th d
Proposed scheme : SOM classification employing training data
SOM (Self Organizing Map): Unsupervised neural network method
⇒ Supervised SOM
Other feature Periodical structure of SOM
1. Torus SOM : Periodical map structure
Other feature
: Periodical map structure ⇒ Avoiding undesired bias of node
2 BLSOM (Batch Leaning SOM)2. BLSOM (Batch Leaning SOM): Impervious to the order of training sequence
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Proposed ATR Method(Actual SOM training procedure)I iti l t t t d i d fi d( ) ∑∑ ==
= trtr
11tr )()(0; N
k kN
k kk aa pxppyInitial output vector on node p is defined p : Location of node
trkx : Training SAR image
( ) tr;maxarg)(ˆ tt xpyp =For k th tranning data , winner node is determined)(ˆ tkptr
kx( );maxarg)( kk tt xpyp
p−=
Ω∈
( ) ( ) ( )( )∑ −tr
1tr ;)),(ˆ(N
k kk tth pyxpp
After calculating for all training data, The output of each node is updated by :
)(ˆ tkp
( ) ( ) ( )( )∑
∑=
=+=+tr
1
1
)),(ˆ(
;)),((;1; N
k k
k kk
thtt
pp
pypppypy
⎟⎞
⎜⎛ −)(ˆ
ˆtk pp )()( tt σβ : Monotonically
⎟⎟⎠
⎞⎜⎜⎝
⎛= 2)(2
)(exp)()),(ˆ(
tt
tth kk σ
βpp
pp )(),( tt σβ : Monotonically decreasing for t
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Proposed ATR Method(Classification Phase)
Cl ifi i P i i lClassification Principle : Assessing value of integral of U-matrix field from training node
( ) xpyxp −= som;minarg)(ˆ TWinner node for test data x
U-matrix field on SOM
p Ω∈
Optimal class number is determined :
{ }∫≤≤
=),(),(1
somopt d)(minminarg
trxx
pkCkCNk
sUK
)( pU : U-matrix potential at node p),( xkC : Possible path from k th training node ),( p g
to winner node )(ˆ xp
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Proposed ATR Method(Classification Phase: U-matrix metric)
Cl ifi i P i i lClassification Principle : Assessing value of integral of U-matrix field from training node
( ) xpyxp −= som;minarg)(ˆ TWinner node for test data x
p Ω∈
Optimal class number is determined :
{ }∫≤≤
=),(),(1
somopt d)(minminarg
trxx
pkCkCNk
sUK
)( pU : U-matrix potential at node p),( xkC : Possible path from k th training node ),( p g
to winner node )(ˆ xp U-matrix field
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Procedure of Proposed MethodProcedure of Proposed Method
BLSOM
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Experimental Validation Experimental setup :1/200 downscaled model of X-band radar
except for center frequency
・Horn antennas (Beamwidth : 27 deg) ・Frequency range: 24GHz – 40GHz ・Slant range resolution : 9.375 mm ・Aperture Length : 1600 mmSlant range resolution : 9.375 mm Aperture Length : 1600 mm・Off-nadir angle : 54.7 degree ・Tx and Rx Separation : 48 mm
Optical image for 5 civilian airplanes Observation scene in experimentB747 B787
Optical image for 5 civilian airplanes Observation scene in experiment
A320DC-10B777
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Evaluation in Noisy Situationva ua o No sy S ua oGaussian noises are numerically
Correct classification probabilityy
added to SAR images for contaminated image generation
U-matrix potential distribution
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Robustness to Observation Angle ErrorsRobustness to Observation Angle ErrorsAngle observation error : φ
φφ = 0 deg
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Robustness to Observation Angle ErrorsRobustness to Observation Angle ErrorsAngle observation error : φ
φφ = 15 deg
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ConclusionConclusion• Accurate ATR method based on Supervised SOM• Accurate ATR method based on Supervised SOM
has been proposed P d th d・Proposed method1. Supervised SOM for ATR classification issue2 New classification metric by using U-matrix metric2. New classification metric by using U-matrix metric
・Experimental validation : ・Correct classification even in under SNR=10 dB・Robust feature for observation angle errors
Future work ・More accurate method exploiting complex value of SAR imageMore accurate method exploiting complex value of SAR image