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Automatic Target Recognition with Support Vector Machines

Automatic Target Recognition with Support Vector Machines

Qun Zhao, Jose Principe

Computational Neuro-Engineering LaboratoryDepartment of Electrical and Computer Engineering

University of Florida

December 4, 1998

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OverviewOverview

Introduction to SAR ATR

4 Classifiers

Conclusions

Experiment results

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1. Introduction1. Introduction

Recognition of vehicles in synthetic aperture radar (SAR) is a

difficult problem due to the low resolution of the sensor (1 meter)

and the speckle (noise) intrinsic to the image formation.

Another difficulty is due to the operating conditions. Vehicles can

be placed in high clutter backgrounds, partial occluded, and NEW

vehicles may be found that were not used in the training set.

Training data is always limited. We use here the MSTAR I and II

database (Veda).

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1. Data Examples 1. Data Examples

BMP2

BTR72

T72

DS1

D7

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2. Four Classifiers2. Four Classifiers

1). Perceptron with hard limiter (perceptron training)

2). Perceptron with sigmoids (delta rule)

)(sgn ji

ijij xwy

)exp(1

1)(

)(

xxf

xwfy ji

ijij

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2. Four Classifiers2. Four Classifiers

3). Optimal Separating Hyperplane

bxxyxfvs

iii ..

)()(

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2. Four Classifiers2. Four Classifiers

4). Support vector machine

Training: kernel-Adatron (FrieB, T., Cristianini, N., and Campbell, C. 1998).

Use Gaussian Kernel.

bxxKyxfvs

iii ..

),()(

),()()()(

)()(:

iii

n

xxKxxxx

featureinputR

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3. Experiments3. Experiments

3 Target classes:

T72, BTR70, and BMP2

Pairwise classification

Image sizes 80 x 80. Aspect 0 ~ 180 degrees.

Training: 17 degree depression

Number of Training samples: 406

Testing: 15 degree depression

Number of Testing samples: 724

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3. Experiments3. Experiments

1. Classification Table Comparison of classification results between classifiers (error rate)

BMP2 BTR70 T72 Total

Perceptron (hardlimiter)

56.77 32.71 45.93 48.62

Perceptron(sigmoid)

11.94 0 2.28 6.08

Optimalhyperplane

8.71 0 2.93 4.97

SVM 7.42 0.93 5.86 5.80

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3. Experiments - Recognition3. Experiments - Recognition

Added two more vehicles to test set. They are called confusers.

Confusers: 2S1 and D7

Number of confuser images : 275

This becomes a recognition problem. The point PD=0.9 of the receiver operating characteristics (ROC) is chosen for the comparison. Output of classifiers are thresholded to achieve PD=0.9.

Now performance is measured by error rate and false alarms.

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3. Experiments - Recognition3. Experiments - Recognition

Table Comparison of recognition results between classifiers(Pe: error rate %; Pfa: probability of false alarm)

BMP2Pe

BTR70Pe

T72Pe

TotalPe

ConfuserPfa

Perceptron(hard limiter)

39.41 30.84 49.68 42.54 85.48

Perceptron(sigmoid)

1.63 0 5.81 3.18 76.00

Optimalhyperplane

0.65 0 3.23 1.66 61.82

SVM 0.98 0 3.23 1.80 55.27

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4. Conclusion4. Conclusion

Classification and recognition are different problems, and the latter is more realistic (and hard).

SVMs with the Gaussian kernel perform better for recognition. The local shape of the Gaussian kernel is very useful and should be utilized (samples that are far away from the class centers tend to have small feature values).

In our problem (large input space) the optimal separating hyperplane performs better for classification.

Kernel-Adatron: easy and fast training