SAR ATR with the TPS - Heriot
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Transcript SAR ATR with the TPS - Heriot
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
Military
Medical
SAR images
MSTAR image
database
Courtesy of Sandia National Laboratory
Synthetic Aperture Radar
SAR instruments use pulses of microwaves
as an active source of illumination
Benefits
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
Five Stages
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
Edge Detection, Topographical Primal
Sketch
Image Matching
Hausdorff Distance Transform
Feature Extraction
Feature Detection
Edge Detection – Sobel Mask
Line Detection – Laplacian Mask
Topographical Primal Sketch
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
Model templates
Problems
Scale
Rotation
Partially obstructed images
Distance Transform
Image Matching procedure
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
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
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
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