synthetic aperture radar automatic target recognition
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Synthetic Aperture Radar Automatic Target Recognition. -Computer Science Department- California 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. - PowerPoint PPT PresentationTRANSCRIPT
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
TemplatesFound
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 DetectionThe box provides little clue for The box provides little clue for
identificationidentificationEven 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 IllustrationIllustrationa2
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 Directions
Feature ExtractionFeature ExtractionFeature 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 Group
http://www.cs.cornell.edu/visionhttp://www.cs.cornell.edu/visionRobert 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
Questions and CommentsQuestions and CommentsVisit our web page Visit our web page
Alvin: Alvin: www.csc.calpoly.edu/~aywangwww.csc.calpoly.edu/~aywangHuey: Huey: www.calpoly.edu/~cyaowww.calpoly.edu/~cyao