Abstract

The use of topographic features is proposed for classifying targets in synthetic-aperture radar (SAR) images. These features are based on the curvature properties of a surface model that is used to estimate the underlying image intensity. The stability of these features with respect to aspect, sensor noise, and squint is investigated. Attention is given to ensuring appropriate registration of the SAR images and to the modeling of noise in the SAR imaging process. Sensitivity and stability of features are quantitatively and qualitatively analyzed, based on each pixel’s label and on the relative groupings of features in the corresponding images. Stability results are presented for images obtained from XPATCH simulation software as well from as the Moving and Stationary Target Recognition target data set. The stability of these features is compared with the stability of features obtained directly from the SAR magnitude images.

© 1999 Optical Society of America

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  1. L. M. Novak, G. J. Owirka, C. M. Netishen, “Performance of a high-resolution polarimetric SAR automatic target recognition system,” Lincoln Lab. J. 6, 11–23 (1993).
  2. A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).
  3. K. Ikeuchi, T. Shakunaga, M. Wheeler, T. Yamazaki, “Invariant histograms and deformable template matching for SAR target recognition,” presented at the Conference on Computer Vision and Pattern Recognition, June 18–20, 1996, San Francisco, Calif.
  4. S. M. Verbout, W. W. Irving, A. S. Hanes, “Improving a template-based classifier in a SAR automatic target recognition system by using 3-D target information,” Lincoln Lab. J. 6, 53–75 (1993).
  5. B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.
  6. L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
    [CrossRef]
  7. G. J. Ettinger, G. A. Klanderman, W. M. Wells, E. L. Grimson, “A probabilistic optimization approach to SAR feature matching,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 318–329 (1996).
    [CrossRef]
  8. T. W. Ryan, B. Egaas, “SAR target indexing with hierarchical distance transforms,” in Algorithms for Synthetic Aperture Radar Imagery III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 243–252 (1996).
    [CrossRef]
  9. B. Wang, T. O. Binford, “Generic, model-based estimation and detection of peaks in image surfaces,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 913–922.
  10. R. Meth, R. Chellappa, “Target indexing in synthetic aperture radar imagery using topographic features,” in Proceedings, International Conference on Acoustics, Speech and Signal Processing (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1996), pp. 2154–2158.
  11. R. M. Haralick, L. T. Watson, T. J. Laffey, “The topographic primal sketch,” Int. J. Robot. Res. 2, 50–72 (1983).
    [CrossRef]
  12. E. R. Keydel, S. W. Lee, “Signature prediction for model-based automatic target recognition,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 306–317 (1996).
    [CrossRef]
  13. L. T. Watson, T. J. Laffey, R. M. Haralick, “Topographic classification of digital intensity surfaces using generalized splines and the discrete cosine transformation,” Comput. Vis. Graph. Image Process. 29, 143–167 (1985).
    [CrossRef]
  14. L. Wang, T. Pavlidis, “Direct gray-scale extraction of features for character recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1053–1067 (1993).
    [CrossRef]
  15. S. W. Lee, Y. J. Kim, “Direct extraction of topographic features for gray scale character recognition,” IEEE Trans. Pattern. Anal. Mach. Intell. 17, 724–729 (1995).
    [CrossRef]
  16. R. M. Haralick, “Ridges and valleys on digital images,” Comput. Vis. Graph. Image Process. 22, 28–38 (1983).
    [CrossRef]
  17. W. J. Rugh, Linear Systems Theory (Prentice-Hall, Englewood Cliffs, N.J., 1996).
  18. R. M. Haralick, “Digital step edges from zero crossing of second directional derivatives,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 58–68 (1984).
    [CrossRef]
  19. S. Kuttikkad, R. Meth, R. Chellappa, “Registration and exploitation of multipass airborne synthetic aperture radar images,” (Center for Automation Research, University of Maryland, College Park, Md., 1997).
  20. S. Kuttikkad, W. Phillips, S. Mathieu-Marni, R. Meth, R. Chellappa, “Use of context for false alarm reduction in SAR automation target recognition,” presented at the Image Understanding Workshop, May 11–14, 1997, New Orleans, La.
  21. D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.
  22. S. Kuttikkad, R. Chellappa, “Non-Gaussian CFAR techniques for target detection in high resolution SAR images,” in Proceedings, IEEE International Conference on Image Processing (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1994), pp. 910–914.
  23. M. I. Skolnik, Introduction to Radar Systems, 2nd ed. (McGraw-Hill, New York, 1980).
  24. S. R. DeGraaf, “SAR Imaging via modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).
    [CrossRef]
  25. A. Leon-Garcia, Probability and Random Processes for Electrical Engineering (Addison-Wesley, New York, 1989).

1998 (1)

S. R. DeGraaf, “SAR Imaging via modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).
[CrossRef]

1995 (2)

L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
[CrossRef]

S. W. Lee, Y. J. Kim, “Direct extraction of topographic features for gray scale character recognition,” IEEE Trans. Pattern. Anal. Mach. Intell. 17, 724–729 (1995).
[CrossRef]

1993 (4)

L. Wang, T. Pavlidis, “Direct gray-scale extraction of features for character recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1053–1067 (1993).
[CrossRef]

L. M. Novak, G. J. Owirka, C. M. Netishen, “Performance of a high-resolution polarimetric SAR automatic target recognition system,” Lincoln Lab. J. 6, 11–23 (1993).

A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).

S. M. Verbout, W. W. Irving, A. S. Hanes, “Improving a template-based classifier in a SAR automatic target recognition system by using 3-D target information,” Lincoln Lab. J. 6, 53–75 (1993).

1985 (1)

L. T. Watson, T. J. Laffey, R. M. Haralick, “Topographic classification of digital intensity surfaces using generalized splines and the discrete cosine transformation,” Comput. Vis. Graph. Image Process. 29, 143–167 (1985).
[CrossRef]

1984 (1)

R. M. Haralick, “Digital step edges from zero crossing of second directional derivatives,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 58–68 (1984).
[CrossRef]

1983 (2)

R. M. Haralick, “Ridges and valleys on digital images,” Comput. Vis. Graph. Image Process. 22, 28–38 (1983).
[CrossRef]

R. M. Haralick, L. T. Watson, T. J. Laffey, “The topographic primal sketch,” Int. J. Robot. Res. 2, 50–72 (1983).
[CrossRef]

Ahn, J.

B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.

Andersh, D. J.

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

Bahnu, B.

B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.

Beckner, F. L.

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

Bernardon, A. M.

A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).

Binford, T. O.

B. Wang, T. O. Binford, “Generic, model-based estimation and detection of peaks in image surfaces,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 913–922.

Carriere, R.

L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
[CrossRef]

Chellappa, R.

R. Meth, R. Chellappa, “Target indexing in synthetic aperture radar imagery using topographic features,” in Proceedings, International Conference on Acoustics, Speech and Signal Processing (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1996), pp. 2154–2158.

S. Kuttikkad, R. Chellappa, “Non-Gaussian CFAR techniques for target detection in high resolution SAR images,” in Proceedings, IEEE International Conference on Image Processing (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1994), pp. 910–914.

S. Kuttikkad, R. Meth, R. Chellappa, “Registration and exploitation of multipass airborne synthetic aperture radar images,” (Center for Automation Research, University of Maryland, College Park, Md., 1997).

S. Kuttikkad, W. Phillips, S. Mathieu-Marni, R. Meth, R. Chellappa, “Use of context for false alarm reduction in SAR automation target recognition,” presented at the Image Understanding Workshop, May 11–14, 1997, New Orleans, La.

Chiang, D.

L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
[CrossRef]

DeGraaf, S. R.

S. R. DeGraaf, “SAR Imaging via modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).
[CrossRef]

Egaas, B.

T. W. Ryan, B. Egaas, “SAR target indexing with hierarchical distance transforms,” in Algorithms for Synthetic Aperture Radar Imagery III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 243–252 (1996).
[CrossRef]

Ettinger, G. J.

G. J. Ettinger, G. A. Klanderman, W. M. Wells, E. L. Grimson, “A probabilistic optimization approach to SAR feature matching,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 318–329 (1996).
[CrossRef]

Fay, D. A.

A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).

Gerry, M. J.

L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
[CrossRef]

Gilkey, M.

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

Grimson, E. L.

G. J. Ettinger, G. A. Klanderman, W. M. Wells, E. L. Grimson, “A probabilistic optimization approach to SAR feature matching,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 318–329 (1996).
[CrossRef]

Hanes, A. S.

S. M. Verbout, W. W. Irving, A. S. Hanes, “Improving a template-based classifier in a SAR automatic target recognition system by using 3-D target information,” Lincoln Lab. J. 6, 53–75 (1993).

Haralick, R. M.

L. T. Watson, T. J. Laffey, R. M. Haralick, “Topographic classification of digital intensity surfaces using generalized splines and the discrete cosine transformation,” Comput. Vis. Graph. Image Process. 29, 143–167 (1985).
[CrossRef]

R. M. Haralick, “Digital step edges from zero crossing of second directional derivatives,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 58–68 (1984).
[CrossRef]

R. M. Haralick, “Ridges and valleys on digital images,” Comput. Vis. Graph. Image Process. 22, 28–38 (1983).
[CrossRef]

R. M. Haralick, L. T. Watson, T. J. Laffey, “The topographic primal sketch,” Int. J. Robot. Res. 2, 50–72 (1983).
[CrossRef]

Hazlett, M.

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

Ikeuchi, K.

K. Ikeuchi, T. Shakunaga, M. Wheeler, T. Yamazaki, “Invariant histograms and deformable template matching for SAR target recognition,” presented at the Conference on Computer Vision and Pattern Recognition, June 18–20, 1996, San Francisco, Calif.

Irving, W. W.

S. M. Verbout, W. W. Irving, A. S. Hanes, “Improving a template-based classifier in a SAR automatic target recognition system by using 3-D target information,” Lincoln Lab. J. 6, 53–75 (1993).

Jones, G.

B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.

Keydel, E. R.

E. R. Keydel, S. W. Lee, “Signature prediction for model-based automatic target recognition,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 306–317 (1996).
[CrossRef]

Kim, Y. J.

S. W. Lee, Y. J. Kim, “Direct extraction of topographic features for gray scale character recognition,” IEEE Trans. Pattern. Anal. Mach. Intell. 17, 724–729 (1995).
[CrossRef]

Klanderman, G. A.

G. J. Ettinger, G. A. Klanderman, W. M. Wells, E. L. Grimson, “A probabilistic optimization approach to SAR feature matching,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 318–329 (1996).
[CrossRef]

Kuttikkad, S.

S. Kuttikkad, R. Meth, R. Chellappa, “Registration and exploitation of multipass airborne synthetic aperture radar images,” (Center for Automation Research, University of Maryland, College Park, Md., 1997).

S. Kuttikkad, W. Phillips, S. Mathieu-Marni, R. Meth, R. Chellappa, “Use of context for false alarm reduction in SAR automation target recognition,” presented at the Image Understanding Workshop, May 11–14, 1997, New Orleans, La.

S. Kuttikkad, R. Chellappa, “Non-Gaussian CFAR techniques for target detection in high resolution SAR images,” in Proceedings, IEEE International Conference on Image Processing (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1994), pp. 910–914.

Laffey, T. J.

L. T. Watson, T. J. Laffey, R. M. Haralick, “Topographic classification of digital intensity surfaces using generalized splines and the discrete cosine transformation,” Comput. Vis. Graph. Image Process. 29, 143–167 (1985).
[CrossRef]

R. M. Haralick, L. T. Watson, T. J. Laffey, “The topographic primal sketch,” Int. J. Robot. Res. 2, 50–72 (1983).
[CrossRef]

Lee, S. W.

S. W. Lee, Y. J. Kim, “Direct extraction of topographic features for gray scale character recognition,” IEEE Trans. Pattern. Anal. Mach. Intell. 17, 724–729 (1995).
[CrossRef]

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

E. R. Keydel, S. W. Lee, “Signature prediction for model-based automatic target recognition,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 306–317 (1996).
[CrossRef]

Leon-Garcia, A.

A. Leon-Garcia, Probability and Random Processes for Electrical Engineering (Addison-Wesley, New York, 1989).

Li, M.

B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.

Mathieu-Marni, S.

S. Kuttikkad, W. Phillips, S. Mathieu-Marni, R. Meth, R. Chellappa, “Use of context for false alarm reduction in SAR automation target recognition,” presented at the Image Understanding Workshop, May 11–14, 1997, New Orleans, La.

Meth, R.

S. Kuttikkad, W. Phillips, S. Mathieu-Marni, R. Meth, R. Chellappa, “Use of context for false alarm reduction in SAR automation target recognition,” presented at the Image Understanding Workshop, May 11–14, 1997, New Orleans, La.

S. Kuttikkad, R. Meth, R. Chellappa, “Registration and exploitation of multipass airborne synthetic aperture radar images,” (Center for Automation Research, University of Maryland, College Park, Md., 1997).

R. Meth, R. Chellappa, “Target indexing in synthetic aperture radar imagery using topographic features,” in Proceedings, International Conference on Acoustics, Speech and Signal Processing (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1996), pp. 2154–2158.

Netishen, C. M.

L. M. Novak, G. J. Owirka, C. M. Netishen, “Performance of a high-resolution polarimetric SAR automatic target recognition system,” Lincoln Lab. J. 6, 11–23 (1993).

Novak, L. M.

L. M. Novak, G. J. Owirka, C. M. Netishen, “Performance of a high-resolution polarimetric SAR automatic target recognition system,” Lincoln Lab. J. 6, 11–23 (1993).

Owirka, G. J.

L. M. Novak, G. J. Owirka, C. M. Netishen, “Performance of a high-resolution polarimetric SAR automatic target recognition system,” Lincoln Lab. J. 6, 11–23 (1993).

Pavlidis, T.

L. Wang, T. Pavlidis, “Direct gray-scale extraction of features for character recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1053–1067 (1993).
[CrossRef]

Phillips, W.

S. Kuttikkad, W. Phillips, S. Mathieu-Marni, R. Meth, R. Chellappa, “Use of context for false alarm reduction in SAR automation target recognition,” presented at the Image Understanding Workshop, May 11–14, 1997, New Orleans, La.

Potter, L. C.

L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
[CrossRef]

Rugh, W. J.

W. J. Rugh, Linear Systems Theory (Prentice-Hall, Englewood Cliffs, N.J., 1996).

Ryan, T. W.

T. W. Ryan, B. Egaas, “SAR target indexing with hierarchical distance transforms,” in Algorithms for Synthetic Aperture Radar Imagery III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 243–252 (1996).
[CrossRef]

Seibert, M.

A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).

Shakunaga, T.

K. Ikeuchi, T. Shakunaga, M. Wheeler, T. Yamazaki, “Invariant histograms and deformable template matching for SAR target recognition,” presented at the Conference on Computer Vision and Pattern Recognition, June 18–20, 1996, San Francisco, Calif.

Shindel, R.

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

Skolnik, M. I.

M. I. Skolnik, Introduction to Radar Systems, 2nd ed. (McGraw-Hill, New York, 1980).

Verbout, S. M.

S. M. Verbout, W. W. Irving, A. S. Hanes, “Improving a template-based classifier in a SAR automatic target recognition system by using 3-D target information,” Lincoln Lab. J. 6, 53–75 (1993).

Wang, B.

B. Wang, T. O. Binford, “Generic, model-based estimation and detection of peaks in image surfaces,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 913–922.

Wang, L.

L. Wang, T. Pavlidis, “Direct gray-scale extraction of features for character recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1053–1067 (1993).
[CrossRef]

Watson, L. T.

L. T. Watson, T. J. Laffey, R. M. Haralick, “Topographic classification of digital intensity surfaces using generalized splines and the discrete cosine transformation,” Comput. Vis. Graph. Image Process. 29, 143–167 (1985).
[CrossRef]

R. M. Haralick, L. T. Watson, T. J. Laffey, “The topographic primal sketch,” Int. J. Robot. Res. 2, 50–72 (1983).
[CrossRef]

Waxman, A.

A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).

Wells, W. M.

G. J. Ettinger, G. A. Klanderman, W. M. Wells, E. L. Grimson, “A probabilistic optimization approach to SAR feature matching,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 318–329 (1996).
[CrossRef]

Wheeler, M.

K. Ikeuchi, T. Shakunaga, M. Wheeler, T. Yamazaki, “Invariant histograms and deformable template matching for SAR target recognition,” presented at the Conference on Computer Vision and Pattern Recognition, June 18–20, 1996, San Francisco, Calif.

Yamazaki, T.

K. Ikeuchi, T. Shakunaga, M. Wheeler, T. Yamazaki, “Invariant histograms and deformable template matching for SAR target recognition,” presented at the Conference on Computer Vision and Pattern Recognition, June 18–20, 1996, San Francisco, Calif.

Yi, J.

B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.

Yu, C. L.

D. J. Andersh, S. W. Lee, F. L. Beckner, M. Gilkey, R. Shindel, M. Hazlett, C. L. Yu, “XPATCH: a high frequency electromagnetic scattering prediction code using shooting and bouncing rays,” in Proceedings, 10th Annual Review of Progress in Applied Computational Electromagnetics, (Applied Computational Electromagnetics Society, Monterey, Calif., 1994), pp. 424–433.

Comput. Vis. Graph. Image Process. (2)

L. T. Watson, T. J. Laffey, R. M. Haralick, “Topographic classification of digital intensity surfaces using generalized splines and the discrete cosine transformation,” Comput. Vis. Graph. Image Process. 29, 143–167 (1985).
[CrossRef]

R. M. Haralick, “Ridges and valleys on digital images,” Comput. Vis. Graph. Image Process. 22, 28–38 (1983).
[CrossRef]

IEEE Trans. Antennas Propag. (1)

L. C. Potter, D. Chiang, R. Carriere, M. J. Gerry, “A GTD-based parametric model for radar scattering,” IEEE Trans. Antennas Propag. 43, 1058–1068 (1995).
[CrossRef]

IEEE Trans. Image Process. (1)

S. R. DeGraaf, “SAR Imaging via modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

L. Wang, T. Pavlidis, “Direct gray-scale extraction of features for character recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1053–1067 (1993).
[CrossRef]

R. M. Haralick, “Digital step edges from zero crossing of second directional derivatives,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 58–68 (1984).
[CrossRef]

IEEE Trans. Pattern. Anal. Mach. Intell. (1)

S. W. Lee, Y. J. Kim, “Direct extraction of topographic features for gray scale character recognition,” IEEE Trans. Pattern. Anal. Mach. Intell. 17, 724–729 (1995).
[CrossRef]

Int. J. Robot. Res. (1)

R. M. Haralick, L. T. Watson, T. J. Laffey, “The topographic primal sketch,” Int. J. Robot. Res. 2, 50–72 (1983).
[CrossRef]

Lincoln Lab. J. (3)

L. M. Novak, G. J. Owirka, C. M. Netishen, “Performance of a high-resolution polarimetric SAR automatic target recognition system,” Lincoln Lab. J. 6, 11–23 (1993).

A. Waxman, M. Seibert, A. M. Bernardon, D. A. Fay, “Neural systems for automatic target learning and recognition,” Lincoln Lab. J. 6, 77–115 (1993).

S. M. Verbout, W. W. Irving, A. S. Hanes, “Improving a template-based classifier in a SAR automatic target recognition system by using 3-D target information,” Lincoln Lab. J. 6, 53–75 (1993).

Other (14)

B. Bahnu, G. Jones, J. Ahn, M. Li, J. Yi, “Recognition of articulated objects in SAR images,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 1237–1250.

K. Ikeuchi, T. Shakunaga, M. Wheeler, T. Yamazaki, “Invariant histograms and deformable template matching for SAR target recognition,” presented at the Conference on Computer Vision and Pattern Recognition, June 18–20, 1996, San Francisco, Calif.

G. J. Ettinger, G. A. Klanderman, W. M. Wells, E. L. Grimson, “A probabilistic optimization approach to SAR feature matching,” in Algorithms for Synthetic Aperture Radar III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 318–329 (1996).
[CrossRef]

T. W. Ryan, B. Egaas, “SAR target indexing with hierarchical distance transforms,” in Algorithms for Synthetic Aperture Radar Imagery III, E. G. Zelnio, R. J. Douglass, eds., Proc. SPIE2757, 243–252 (1996).
[CrossRef]

B. Wang, T. O. Binford, “Generic, model-based estimation and detection of peaks in image surfaces,” in Proceedings of the ARPA Image Understanding Workshop (Morgan Kaufmann, San Francisco, Calif., 1996), pp. 913–922.

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Figures (10)

Fig. 1
Fig. 1

T72 tank: (a) XPATCH simulated SAR image, (b) corresponding TPS image, (c) color map.

Fig. 2
Fig. 2

SAR imaging geometry.

Fig. 3
Fig. 3

Key to stability figures.

Fig. 4
Fig. 4

XPATCH simulated data set: stability of features with respect to azimuth. (a), (c), (e) Matches in 3×3 neighborhood. (b), (d), (f) Exact matches. (a), (b) Total matches (scaled by maximum number of features). (c), (d) Standard deviation. (e), (f) Intertarget stability subtracted (and scaled by maximum number of features; see Subsection 5.A).

Fig. 5
Fig. 5

XPATCH simulated data set: stability of features with respect to elevation. (a), (c), (e) Matches in 3×3 neighborhood. (b), (d), (f) Exact matches. (a), (b) Total matches (scaled by maximum number of features). (c), (d) Standard deviation. (e), (f) Intertarget stability subtracted (and scaled by maximum number of features; see Subsection 5.A).

Fig. 6
Fig. 6

T62 tank: ridges are shown in black. (a) Azimuth 5°, (b) azimuth 85°, (c) SNR of 40 dB, (d) SNR of 30 dB.

Fig. 7
Fig. 7

XPATCH simulated data set: stability of features with respect to squint. (a), (c), (e) Matches in 3×3 neighborhood. (b), (d), (f) Exact matches. (a), (b) Total matches (scaled by maximum number of features). (c), (d) Standard deviation. (e), (f) Intertarget stability subtracted (and scaled by maximum number of features; see Subsection 5.A).

Fig. 8
Fig. 8

XPATCH simulated data set: stability of features with respect to noise. (a), (c), (e) Matches in 3×3 neighborhood. (b), (d), (f) Exact matches. (a), (b) Total matches (scaled by maximum number of features). (c), (d) Standard deviation. (e), (f) Intertarget stability subtracted (and scaled by maximum number of features; see Subsection 5.A).

Fig. 9
Fig. 9

MSTAR data set: stability of features with respect to azimuth. (a), (c), (e) Matches in 3×3 neighborhood. (b), (d), (f) Exact matches. (a), (b) Total matches (scaled by maximum number of features). (c), (d) Standard deviation. (e), (f) Intertarget stability subtracted (and scaled by maximum number of features; see Subsection 5.A).

Fig. 10
Fig. 10

SAR spotlight imaging geometry.

Tables (2)

Tables Icon

Table 1 TPS Classification Based on Values at Zero Crossings of the First Directional Derivative

Tables Icon

Table 2 MSTAR Data Set: Stability of Features with Respect to Elevationa

Equations (36)

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f(r, c)=k1+k2r+k3c+k4r2+k5rc+k6c2+k7r3+k8r2c+k9rc2+k10c3.
A=(ATA)-1AT.
fβ(r, c)=limh0 f[r+h sin(β), c+h cos(β)]-f(r, c)h,
fβ(r, c)=fr(r, c)sin(β)+fc(r, c)cos(β).
fβ(r, c)=2fr2(r, c)sin2(β)+2 2frc(r, c)sin(β)cos(β)+2fc2(r, c)cos2(β).
f(r, c)=fr(r, c)fc(r, c)t,
H=2fr22frc2fcr2fc2.
λminxxtHxλmaxx,xR2,
fv(ρ)=Aρ3+Bρ2+Cρ+D,
A=k7v1+k8v12v2+k9v22v1+k10v23,
B=k4v12+k5v1v2+k6v22,
C=k2v1+k3v2.
fv(ρ)=3Aρ2+2Bρ+C,fv(ρ)=6Aρ+2B
fω=f·ω,
fω=ωtHω,
fω=0,fω<0 ω.
p(2)=Ap(1)+b,
A=1δc(2)001δr(2)100cos θ(2)cos ϕ-sin ϕsin ϕcos ϕ×1001cos θ(1)δc(1)00δr(1);
p˜(2)=Pn2TW2R2{RW1W2[nW1tn1+TR1W1p˜(1)]+tg},
p˜(i)=[r(i)c(i)0]T
TRiWi=cos θ cos ψ-k sin ψ cos ψ cos2 θk sin θcos θ sin ψ1/k0-sin θk sin ψ cos θ sin θk cos θ cos ψ,k=1/sqrt(sin2 θ+cos2 θ cos2 ψ)
nW1=thirdcolumnofTR1W1
tn1=[-1/nW1(3)][TR1W1p˜(1)](3)
RW1W2=cos(β)sin(β)0-sin(β)cos(β)0001,β=xˆ1xˆ2
TWiRi=TRiWit
Pni=100010000
p˜W1(1)=TR1W1p˜(1)
x=p˜W1(1)(1)+nW1(1)t,y=p˜W1(1)(2)+nW1(2)t,
z=p˜W1(1)(3)+nW1(3)t
I(r, c)=R(r, c)+N(r, c)=f(r, c)+E(r, c)+N(r, c),
I(r, c)f(r, c)+N(r, c).
hk=AHn=1Nn=0N-1Hn exp(-2πikn/N),
[exp(-2πikn/N)]k=0N-1,n=0N-1.
Kh=AKHA*,
AA*=n=0N-1 exp(-2πik1n/N)exp(2πik2n/N)=n=0N-1 exp[2πin/N(k1-k2)].
n=0N-1 exp[2πin/N(k1-k2)]=Nk1=k20k1k2.

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