Synthetic Aperture Radar Synthetic Aperture Radar Automatic Target RecognitionAutomatic Target Recognition

-Computer Science Department--Computer Science Department-California Polytechnic State University,California Polytechnic State University,

San Luis ObispoSan Luis Obispo

Alvin Y. WangAlvin Y. Wang and and Chia-Huei YaoChia-Huei Yao

Faculty Advisor: Faculty Advisor: Dr. John SaghriDr. John Saghri

Project Sponsor: Project Sponsor: Raytheon CompanyRaytheon CompanyContact Personnel: Contact Personnel: Jeff HoffnerJeff Hoffner

AgendaAgenda

IntroductionIntroductionAutomatic Target RecognitionAutomatic Target RecognitionSynthetic Aperture RadarSynthetic Aperture RadarProblem and Proposed SolutionsProblem and Proposed SolutionsFeature ExtractionFeature Extraction Image MatchingImage MatchingConclusionConclusion

IntroductionIntroduction

Usage of image identificationUsage of image identification MilitaryMilitary MedicalMedical

SAR imagesSAR images MSTAR image MSTAR image

databasedatabase

Courtesy of Sandia National Laboratory

Synthetic Aperture Radar Synthetic Aperture Radar SAR instruments use pulses of microwaves SAR instruments use pulses of microwaves

as an active source of illuminationas an active source of illumination BenefitsBenefits

Independent of light sourcesIndependent of light sources Capable to see through cloudsCapable to see through clouds Spatial resolution remains the same no matter Spatial resolution remains the same no matter

how far the target area ishow far the target area is

Five Stages Five Stages Feature Extraction – DetectionFeature Extraction – Detection Feature Enhancement - DiscriminationFeature Enhancement - Discrimination Image Matching – Classification, Recognition, & Image Matching – Classification, Recognition, &

IdentificationIdentification

Input Image

Feature

Extraction

Feature

Enhancement

Noise and Nonfeature

Target

Classification

Database

Templates

Found

Not Found

Automated Target RecognitionAutomated Target Recognition

Problem and Proposed Problem and Proposed SolutionsSolutions

Traditional ATR algorithmsTraditional ATR algorithmsProblem: Removal of useful target informationProblem: Removal of useful target information

Solution: Multi-feature ATR techniquesSolution: Multi-feature ATR techniques Feature ExtractionFeature Extraction

Edge Detection, Topographical Primal SketchEdge Detection, Topographical Primal Sketch Image MatchingImage Matching

Hausdorff Distance TransformHausdorff Distance Transform

Feature ExtractionFeature Extraction

Feature DetectionFeature Detection Edge Detection – Sobel MaskEdge Detection – Sobel Mask Line Detection – Laplacian MaskLine Detection – Laplacian Mask

Topographical Primal SketchTopographical Primal Sketch Multiple-feature considerationMultiple-feature consideration

Wait…Before Feature Wait…Before Feature DetectionDetection

Reject NoiseReject NoiseThe target images are full of noiseThe target images are full of noiseMedian filterMedian filter

Edge DetectionEdge Detection

The box provides little clue for The box provides little clue for identificationidentification

Even worse, the edges are affected Even worse, the edges are affected by different illuminating status and by different illuminating status and orientation orientation

SAR image Extracted Edge (before threshold) T72 Tank in different orientation

Topographical Primal SketchTopographical Primal Sketch

The light intensity variations on an image aThe light intensity variations on an image are caused by an object’s surface orientatiore caused by an object’s surface orientation, its reflectance, and characteristics of its n, its reflectance, and characteristics of its lighting sourcelighting source

Based on the variance of light intensity, we Based on the variance of light intensity, we can classify and group the underlying imagcan classify and group the underlying image into some topographical categoriese into some topographical categories

Topographical categories includes: peak, piTopographical categories includes: peak, pit, ridge, ravine, saddle, flat, hillside, etc.t, ridge, ravine, saddle, flat, hillside, etc.

Based on the location of the topographical Based on the location of the topographical features, we can reasonably reconstruct thfeatures, we can reasonably reconstruct the original 3D model.e original 3D model.

Feature Extraction and Distance TransformFeature Extraction and Distance Transform

Original Image

Feature Extraction

Edge

Peak

Ridge

Distance Transform

DatabaseDatabaseModel templatesModel templates

ProblemsProblemsScaleScaleRotationRotationPartially obstructed imagesPartially obstructed images

Distance TransformDistance Transform

Image MatchingImage Matching

Image Matching procedureImage Matching procedure Find contour points of the reference shape and oFind contour points of the reference shape and o

btain their DTbtain their DT Obtain contour points of the measured shapeObtain contour points of the measured shape Compute and superimpose the centroids of the tCompute and superimpose the centroids of the t

wo point setswo point sets Rotate and translate the measured point set with Rotate and translate the measured point set with

respect to the initial poserespect to the initial pose Select those relative positions that yield the miniSelect those relative positions that yield the mini

mum HD valuemum HD value Select the one with the least mean HD.Select the one with the least mean HD.

Hausdorff Distance TransformHausdorff Distance Transform

h(A,B) = max {min { d(a,b)} }h(A,B) = max {min { d(a,b)} } H(A,B) = max {h(A,B), h(B,A)}H(A,B) = max {h(A,B), h(B,A)}

Hausdorff Distance Hausdorff Distance IllustrationIllustration

a2

a1

b1

b2

b3

h(A,B)

h(B,A)H(A,B)

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.

Chamfer Distance Chamfer Distance TransformTransform

CDT Provides good approximation to the exact CDT Provides good approximation to the exact Euclidean distanceEuclidean distance

Distance Trasform converts a binary image to another Distance Trasform converts a binary image to another image in which pixel value is the distance from this image in which pixel value is the distance from this pixel to the nearest nonzero pixel of the binary image.pixel to the nearest nonzero pixel of the binary image.

courtesy of IPAN

Image Matching procedureImage Matching procedure

Image Matching procedureImage Matching procedure Find contour points of the reference shape and oFind contour points of the reference shape and o

btain their DTbtain their DT Obtain contour points of the measured shapeObtain contour points of the measured shape Compute and superimpose the centroids of the tCompute and superimpose the centroids of the t

wo point setswo point sets Rotate and translate the measured point set with Rotate and translate the measured point set with

respect to the initial poserespect to the initial pose Select those relative positions that yield the miniSelect those relative positions that yield the mini

mum HD valuemum HD value Select the one with the least mean HD.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

ConclusionConclusion

Current Progress and Future DirectionsCurrent Progress and Future DirectionsFeature ExtractionFeature Extraction

Feature detectionFeature detectionTPSTPS

Image MatchingImage MatchingHausdorff Distance TransformHausdorff Distance Transform

TestingTestingDatabaseDatabaseActual Matching with test imagesActual Matching with test images

ReferencesReferences

Image and Pattern Analysis Group – Image and Pattern Analysis Group – http://visual.ipan.sztaki.hu/http://visual.ipan.sztaki.hu/

Cornell Computer Vision GroupCornell Computer Vision Grouphttp://www.cs.cornell.edu/visionhttp://www.cs.cornell.edu/vision

Robert M. Haralick, Layne T. Watson, Robert M. Haralick, Layne T. Watson, Thomas J. Laffey, The Topographic Thomas J. Laffey, The Topographic Primal Sketch. The international Primal Sketch. The international Journal of Robotics Research. Vol. 2, Journal of Robotics Research. Vol. 2, No. 1, Spring No. 1, Spring 19831983

Thank YouThank You

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Alvin: Alvin: www.csc.calpoly.edu/~aywangwww.csc.calpoly.edu/~aywangHuey: Huey: www.calpoly.edu/~cyaowww.calpoly.edu/~cyao