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Synthetic Aperture Radar Automatic Target Recognition

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.

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Synthetic Aperture Radar Automatic Target Recognition

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  1. 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 SaghriProject Sponsor: Raytheon CompanyContact Personnel: Jeff Hoffner

  2. Agenda • Introduction • Automatic Target Recognition • Synthetic Aperture Radar • Problem and Proposed Solutions • Feature Extraction • Image Matching • Conclusion

  3. Introduction • Usage of image identification • Military • Medical • SAR images • MSTAR image database Courtesy of Sandia National Laboratory

  4. 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

  5. Database Input Image Templates Found Feature Extraction Feature Enhancement Target Classification Not Found Noise and Nonfeature Automated Target Recognition • Five Stages • Feature Extraction – Detection • Feature Enhancement - Discrimination • Image Matching – Classification, Recognition, & Identification

  6. 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

  7. Feature Extraction • Feature Detection • Edge Detection – Sobel Mask • Line Detection – Laplacian Mask • Topographical Primal Sketch • Multiple-feature consideration

  8. Wait…Before Feature Detection • Reject Noise • The target images are full of noise • Median filter

  9. SAR image Extracted Edge (before threshold)T72 Tank in different orientation Edge Detection • The box provides little clue for identification • Even worse, the edges are affected by different illuminating status and orientation

  10. 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

  11. 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.

  12. Feature Extraction and Distance Transform Edge Feature Extraction Distance Transform Original Image Peak Ridge

  13. Image Matching • Database • Model templates • Problems • Scale • Rotation • Partially obstructed images • Distance Transform

  14. 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.

  15. Hausdorff Distance Transform • h(A,B) = max {min { d(a,b)} } • H(A,B) = max {h(A,B), h(B,A)}

  16. a1 b3 b1 b2 Hausdorff Distance Illustration a2 h(A,B) 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.

  17. 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 pixel to the nearest nonzero pixel of the binary image. courtesy of IPAN

  18. Image Matching procedure

  19. 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.

  20. An image (left) and its distance transform (right) Test image and Target detected when the contours are superimposed courtesy of IPAN

  21. Template image Test image Target detected courtesy of Cornell Vision Group

  22. Conclusion • Current Progress and Future Directions • Feature Extraction • Feature detection • TPS • Image Matching • Hausdorff Distance Transform • Testing • Database • Actual Matching with test images

  23. References • Image and Pattern Analysis Group –http://visual.ipan.sztaki.hu/ • Cornell Computer Vision Grouphttp://www.cs.cornell.edu/vision • Robert M. Haralick, Layne T. Watson, Thomas J. Laffey, The Topographic Primal Sketch. The international Journal of Robotics Research. Vol. 2, No. 1, Spring 1983

  24. Thank You Questions and Comments Visit our web page Alvin: www.csc.calpoly.edu/~aywang Huey: www.calpoly.edu/~cyao

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