Synthetic Aperture Radar
Automatic Target Recognition
-Computer Science DepartmentCalifornia Polytechnic State University,
San Luis Obispo
Alvin Y. Wang and Chia-Huei Yao
Faculty Advisor: Dr. John Saghri
Project Sponsor: Raytheon Company
Contact Personnel: Jeff Hoffner
Agenda
Introduction
 Automatic Target Recognition
 Synthetic Aperture Radar
 Problem and Proposed Solutions
 Feature Extraction
 Image Matching
 Conclusion

Introduction

Usage of image identification
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Military
Medical
SAR images
MSTAR image
database
Courtesy of Sandia National Laboratory
Synthetic Aperture Radar

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SAR instruments use pulses of microwaves
as an active source of illumination
Benefits

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Independent of light sources
Capable to see through clouds
Spatial resolution remains the same no matter
how far the target area is
Automated Target Recognition
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Five Stages
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Feature Extraction – Detection
Feature Enhancement - Discrimination
Image Matching – Classification, Recognition, &
Identification
Database
Input Image
Templates
Feature
Feature
Target
Extraction
Enhancement
Classification
Noise and Nonfeature
Found
Not Found
Problem and Proposed Solutions

Traditional ATR algorithms

Problem: Removal of useful target
information
Solution: Multi-feature ATR techniques
 Feature Extraction
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Edge Detection, Topographical Primal
Sketch
Image Matching

Hausdorff Distance Transform
Feature Extraction

Feature Detection
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Edge Detection – Sobel Mask
Line Detection – Laplacian Mask
Topographical Primal Sketch
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Multiple-feature consideration
Wait…Before Feature Detection

Reject Noise
The target images are full of noise
 Median filter

Edge Detection
The box provides little clue for
identification
 Even worse, the edges are affected by
different illuminating status and
orientation

SAR image
Extracted Edge (before threshold)
T72 Tank in different orientation
Topographical Primal Sketch
The light intensity variations on an
image are caused by an object’s
surface orientation, its reflectance, and
characteristics of its lighting source
 Based on the variance of light intensity,
we can classify and group the
underlying image into some
topographical categories

Topographical categories includes:
peak, pit, ridge, ravine, saddle, flat,
hillside, etc.
 Based on the location of the
topographical features, we can
reasonably reconstruct the original 3D
model.

Feature Extraction and Distance Transform
Edge
Feature
Extraction
Original Image
Distance
Transform
Peak
Ridge
Image Matching

Database

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Model templates
Problems
Scale
 Rotation
 Partially obstructed images
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Distance Transform
Image Matching procedure
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Find contour points of the reference shape
and obtain their DT
Obtain contour points of the measured shape
Compute and superimpose the centroids of
the two point sets
Rotate and translate the measured point set
with respect to the initial pose
Select those relative positions that yield the
minimum HD value
Select the one with the least mean HD.
Hausdorff Distance Transform
h(A,B) = max {min { d(a,b)} }
 H(A,B) = max {h(A,B), h(B,A)}

Hausdorff Distance Illustration
a2
h(A,B)
a1
b3
b1
H(A,B)
h(B,A)
Hausdorff Distance provides a measure
of set A and set B’s proximity – it indicates
the maximal distance between any points
of A to B.
b2
Chamfer Distance Transform
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CDT Provides good approximation to the exact
Euclidean distance
Distance Trasform converts a binary image to another
image in which pixel value is the distance from this pi
xel to the nearest nonzero pixel of the binary image.
courtesy of IPAN
Image Matching procedure
Image Matching procedure
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Find contour points of the reference shape
and obtain their DT
Obtain contour points of the measured shape
Compute and superimpose the centroids of
the two point sets
Rotate and translate the measured point set
with respect to the initial pose
Select those relative positions that yield the
minimum HD value
Select the one with the least mean HD.
An image (left) and its distance transform (right)
Test image and Target detected when the contours are superimposed
courtesy of IPAN
Template image
Test image
Target detected
courtesy of Cornell Vision Group
Conclusion
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Current Progress and Future Directions

Feature Extraction
 Feature
detection
 TPS

Image Matching
 Hausdorff

Distance Transform
Testing
 Database
 Actual
Matching with test images
References

Image and Pattern Analysis Group –
http://visual.ipan.sztaki.hu/
Cornell Computer Vision Group
http://www.cs.cornell.edu/vision
 Robert M. Haralick, Layne T. Watson, Th
omas J. Laffey, The Topographic Primal
Sketch. The international Journal of Rob
otics Research. Vol. 2, No. 1, Spring
1983

Thank You
Questions and Comments
Visit our web page
Alvin: www.csc.calpoly.edu/~aywang
Huey: www.calpoly.edu/~cyao