Abstract

Spectral variability remains a major challenge for target detection in hyperspectral imagery (HSI). Recently, the spectral fringe-adjusted joint transform correlation (SFJTC) technique has been used effectively for hyperspectral target detection applications. In this paper, we propose to use discrete wavelet transform (DWT) coefficients of the signatures as features for detection in order to make the SFJTC technique more insensitive to spectral variability. We devised a supervised training algorithm that uses the pure target signature and randomly selected samples from input scenery to select an optimal set of DWT coefficients for detection. We have inserted target signatures into urban and vegetative hyperspectral scenery with varying levels of spectral variability to explore the performance of our DWT-based SFJTC technique in different operating conditions. Detection results in the form of receiver-operating-characteristic (ROC) curves and area-under-the-ROC (AUROC) curves show that the proposed scheme yields the largest mean AUROC values compared to SFJTC using the original signatures and traditional hyperspectral detection algorithms.

© 2011 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. S. M. Yamany, A. A. Farag, and S.-Y. Hsu, “A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems,” Patt. Recogn. Lett. 20, 1431–1438 (1999).
    [CrossRef]
  2. D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19 (1), 29–43 (2002).
    [CrossRef]
  3. D. Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J. 14, 79–114 (2003).
  4. D. Manolakis, “Taxonomy of detection algorithms for hyperspectral imaging applications,” Opt. Eng. 44, 066403 (2005).
    [CrossRef]
  5. M. S. Alam and S. Ochilov, “Spectral fringe-adjusted joint transform correlation,” Appl. Opt. 49, B18–B25 (2010).
    [CrossRef] [PubMed]
  6. W. Sakla, A. Sakla, and M. S. Alam, “Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation,” (invited paper) Proc. SPIE 6967, 69670B (2008).
    [CrossRef]
  7. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (Kluwer Academic/Plenum, 2003).
  8. C.-I. Chang, “An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis,” IEEE Trans. Inf. Theory 46, 1927–1932(2000).
    [CrossRef]
  9. C. S. Weaver and J. W. Goodman, “A technique for optically convolving two functions,” Appl. Opt. 5, 1248–1249 (1966).
    [CrossRef] [PubMed]
  10. F. T. S. Yu and J. E. Ludman, “Microcomputer based programmable joint transform correlator for automatic pattern recognition and identification,” Opt. Lett. 11, 395–397 (1986).
    [CrossRef] [PubMed]
  11. S. Jutamulia, G. M. Storti, D. A. Gregory, and J. C. Kirsch, “Illumination-independent high-efficiency joint transform correlator,” Appl. Opt. 30, 4173–4175 (1991).
    [CrossRef] [PubMed]
  12. M. S. Alam and M. A. Karim, “Fringe adjusted joint transform correlation,” Appl. Opt. 32, 4344–4350 (1993).
    [CrossRef] [PubMed]
  13. M. S. Alam and M. A. Karim, “Multiple target detection using a modified fringe-adjusted joint transform correlator,” Opt. Eng. 33, 1610–1617 (1994).
    [CrossRef]
  14. M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
    [CrossRef]
  15. R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image compression through wavelet transform coding,” IEEE Trans. Inf. Theory 38, 719–746 (1992).
    [CrossRef]
  16. S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532–1546 (2000).
    [CrossRef]
  17. T. Chang and C. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2, 429–441 (1993).
    [CrossRef] [PubMed]
  18. L. M. Bruce and J. Li, “Wavelets for computationally efficient hyperspectral derivative analysis,” IEEE Trans. Geosci. Remote Sens. 39, 1540–1546 (2001).
    [CrossRef]
  19. L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002).
    [CrossRef]
  20. S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, “Automatic reduction of hyperspectral imagery using wavelet spectral analysis,” IEEE Trans. Geosci. Remote Sens. 41, 863–871(2003).
    [CrossRef]
  21. L. M. Bruce, C. Morgan, and S. Larsen, “Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms,” IEEE Trans. Geosci. Remote Sens. 39, 2217–2226 (2001).
    [CrossRef]
  22. S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989).
    [CrossRef]
  23. M. Vetterli and J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, 1995).
  24. S. Mallat, A Wavelet Tour of Signal Processing, 2nd. ed.(Academic, 1999).
  25. ITRES Research, http://www.itres.com, accessed in 2007.
  26. R. A. Schowengerdt, Remote Sensing, 2nd ed. (Academic, 1997).
  27. I. C. Chein and D. C. Heinz, “Constrained subpixel target detection for remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 38, 1144–1159 (2000).
    [CrossRef]
  28. A. K. Jain, Fundamentals of Digital Image Processing(Prentice-Hall, 1989).

2010 (1)

2008 (1)

W. Sakla, A. Sakla, and M. S. Alam, “Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation,” (invited paper) Proc. SPIE 6967, 69670B (2008).
[CrossRef]

2005 (2)

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

D. Manolakis, “Taxonomy of detection algorithms for hyperspectral imaging applications,” Opt. Eng. 44, 066403 (2005).
[CrossRef]

2003 (2)

D. Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J. 14, 79–114 (2003).

S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, “Automatic reduction of hyperspectral imagery using wavelet spectral analysis,” IEEE Trans. Geosci. Remote Sens. 41, 863–871(2003).
[CrossRef]

2002 (2)

L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002).
[CrossRef]

D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19 (1), 29–43 (2002).
[CrossRef]

2001 (2)

L. M. Bruce and J. Li, “Wavelets for computationally efficient hyperspectral derivative analysis,” IEEE Trans. Geosci. Remote Sens. 39, 1540–1546 (2001).
[CrossRef]

L. M. Bruce, C. Morgan, and S. Larsen, “Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms,” IEEE Trans. Geosci. Remote Sens. 39, 2217–2226 (2001).
[CrossRef]

2000 (3)

I. C. Chein and D. C. Heinz, “Constrained subpixel target detection for remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 38, 1144–1159 (2000).
[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532–1546 (2000).
[CrossRef]

C.-I. Chang, “An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis,” IEEE Trans. Inf. Theory 46, 1927–1932(2000).
[CrossRef]

1999 (1)

S. M. Yamany, A. A. Farag, and S.-Y. Hsu, “A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems,” Patt. Recogn. Lett. 20, 1431–1438 (1999).
[CrossRef]

1994 (1)

M. S. Alam and M. A. Karim, “Multiple target detection using a modified fringe-adjusted joint transform correlator,” Opt. Eng. 33, 1610–1617 (1994).
[CrossRef]

1993 (2)

T. Chang and C. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2, 429–441 (1993).
[CrossRef] [PubMed]

M. S. Alam and M. A. Karim, “Fringe adjusted joint transform correlation,” Appl. Opt. 32, 4344–4350 (1993).
[CrossRef] [PubMed]

1992 (1)

R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image compression through wavelet transform coding,” IEEE Trans. Inf. Theory 38, 719–746 (1992).
[CrossRef]

1991 (1)

1989 (1)

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989).
[CrossRef]

1986 (1)

1966 (1)

Alam, M. S.

M. S. Alam and S. Ochilov, “Spectral fringe-adjusted joint transform correlation,” Appl. Opt. 49, B18–B25 (2010).
[CrossRef] [PubMed]

W. Sakla, A. Sakla, and M. S. Alam, “Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation,” (invited paper) Proc. SPIE 6967, 69670B (2008).
[CrossRef]

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

M. S. Alam and M. A. Karim, “Multiple target detection using a modified fringe-adjusted joint transform correlator,” Opt. Eng. 33, 1610–1617 (1994).
[CrossRef]

M. S. Alam and M. A. Karim, “Fringe adjusted joint transform correlation,” Appl. Opt. 32, 4344–4350 (1993).
[CrossRef] [PubMed]

Bal, A.

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Bruce, L. M.

L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002).
[CrossRef]

L. M. Bruce and J. Li, “Wavelets for computationally efficient hyperspectral derivative analysis,” IEEE Trans. Geosci. Remote Sens. 39, 1540–1546 (2001).
[CrossRef]

L. M. Bruce, C. Morgan, and S. Larsen, “Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms,” IEEE Trans. Geosci. Remote Sens. 39, 2217–2226 (2001).
[CrossRef]

Chang, C.-I.

C.-I. Chang, “An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis,” IEEE Trans. Inf. Theory 46, 1927–1932(2000).
[CrossRef]

C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (Kluwer Academic/Plenum, 2003).

Chang, S. G.

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532–1546 (2000).
[CrossRef]

Chang, T.

T. Chang and C. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2, 429–441 (1993).
[CrossRef] [PubMed]

Chein, I. C.

I. C. Chein and D. C. Heinz, “Constrained subpixel target detection for remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 38, 1144–1159 (2000).
[CrossRef]

DeVore, R. A.

R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image compression through wavelet transform coding,” IEEE Trans. Inf. Theory 38, 719–746 (1992).
[CrossRef]

El-Ghazawi, T.

S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, “Automatic reduction of hyperspectral imagery using wavelet spectral analysis,” IEEE Trans. Geosci. Remote Sens. 41, 863–871(2003).
[CrossRef]

Farag, A. A.

S. M. Yamany, A. A. Farag, and S.-Y. Hsu, “A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems,” Patt. Recogn. Lett. 20, 1431–1438 (1999).
[CrossRef]

Goh, S. F.

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Goodman, J. W.

Gregory, D. A.

Heinz, D. C.

I. C. Chein and D. C. Heinz, “Constrained subpixel target detection for remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 38, 1144–1159 (2000).
[CrossRef]

Horache, E. H.

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Hsu, S.-Y.

S. M. Yamany, A. A. Farag, and S.-Y. Hsu, “A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems,” Patt. Recogn. Lett. 20, 1431–1438 (1999).
[CrossRef]

Jain, A. K.

A. K. Jain, Fundamentals of Digital Image Processing(Prentice-Hall, 1989).

Jawerth, B.

R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image compression through wavelet transform coding,” IEEE Trans. Inf. Theory 38, 719–746 (1992).
[CrossRef]

Jutamulia, S.

Kaewpijit, S.

S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, “Automatic reduction of hyperspectral imagery using wavelet spectral analysis,” IEEE Trans. Geosci. Remote Sens. 41, 863–871(2003).
[CrossRef]

Karim, M. A.

M. S. Alam and M. A. Karim, “Multiple target detection using a modified fringe-adjusted joint transform correlator,” Opt. Eng. 33, 1610–1617 (1994).
[CrossRef]

M. S. Alam and M. A. Karim, “Fringe adjusted joint transform correlation,” Appl. Opt. 32, 4344–4350 (1993).
[CrossRef] [PubMed]

Kirsch, J. C.

Koger, C. H.

L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002).
[CrossRef]

Kovacevic, J.

M. Vetterli and J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, 1995).

Kuo, C.

T. Chang and C. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2, 429–441 (1993).
[CrossRef] [PubMed]

Larsen, S.

L. M. Bruce, C. Morgan, and S. Larsen, “Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms,” IEEE Trans. Geosci. Remote Sens. 39, 2217–2226 (2001).
[CrossRef]

Le Moigne, J.

S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, “Automatic reduction of hyperspectral imagery using wavelet spectral analysis,” IEEE Trans. Geosci. Remote Sens. 41, 863–871(2003).
[CrossRef]

Li, J.

L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002).
[CrossRef]

L. M. Bruce and J. Li, “Wavelets for computationally efficient hyperspectral derivative analysis,” IEEE Trans. Geosci. Remote Sens. 39, 1540–1546 (2001).
[CrossRef]

Loo, C. H.

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Lucier, B. J.

R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image compression through wavelet transform coding,” IEEE Trans. Inf. Theory 38, 719–746 (1992).
[CrossRef]

Ludman, J. E.

Mallat, S.

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989).
[CrossRef]

S. Mallat, A Wavelet Tour of Signal Processing, 2nd. ed.(Academic, 1999).

Manolakis, D.

D. Manolakis, “Taxonomy of detection algorithms for hyperspectral imaging applications,” Opt. Eng. 44, 066403 (2005).
[CrossRef]

D. Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J. 14, 79–114 (2003).

D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19 (1), 29–43 (2002).
[CrossRef]

Marden, D.

D. Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J. 14, 79–114 (2003).

Morgan, C.

L. M. Bruce, C. Morgan, and S. Larsen, “Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms,” IEEE Trans. Geosci. Remote Sens. 39, 2217–2226 (2001).
[CrossRef]

Ochilov, S.

Regula, S. P.

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Sakla, A.

W. Sakla, A. Sakla, and M. S. Alam, “Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation,” (invited paper) Proc. SPIE 6967, 69670B (2008).
[CrossRef]

Sakla, W.

W. Sakla, A. Sakla, and M. S. Alam, “Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation,” (invited paper) Proc. SPIE 6967, 69670B (2008).
[CrossRef]

Schowengerdt, R. A.

R. A. Schowengerdt, Remote Sensing, 2nd ed. (Academic, 1997).

Sharma, A.

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Shaw, G.

D. Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J. 14, 79–114 (2003).

D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19 (1), 29–43 (2002).
[CrossRef]

Storti, G. M.

Vetterli, M.

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532–1546 (2000).
[CrossRef]

M. Vetterli and J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, 1995).

Weaver, C. S.

Yamany, S. M.

S. M. Yamany, A. A. Farag, and S.-Y. Hsu, “A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems,” Patt. Recogn. Lett. 20, 1431–1438 (1999).
[CrossRef]

Yu, B.

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532–1546 (2000).
[CrossRef]

Yu, F. T. S.

Appl. Opt. (4)

IEEE Signal Process. Mag. (1)

D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19 (1), 29–43 (2002).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (5)

L. M. Bruce and J. Li, “Wavelets for computationally efficient hyperspectral derivative analysis,” IEEE Trans. Geosci. Remote Sens. 39, 1540–1546 (2001).
[CrossRef]

L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,” IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002).
[CrossRef]

S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, “Automatic reduction of hyperspectral imagery using wavelet spectral analysis,” IEEE Trans. Geosci. Remote Sens. 41, 863–871(2003).
[CrossRef]

L. M. Bruce, C. Morgan, and S. Larsen, “Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms,” IEEE Trans. Geosci. Remote Sens. 39, 2217–2226 (2001).
[CrossRef]

I. C. Chein and D. C. Heinz, “Constrained subpixel target detection for remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 38, 1144–1159 (2000).
[CrossRef]

IEEE Trans. Image Process. (2)

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532–1546 (2000).
[CrossRef]

T. Chang and C. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2, 429–441 (1993).
[CrossRef] [PubMed]

IEEE Trans. Inf. Theory (2)

R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image compression through wavelet transform coding,” IEEE Trans. Inf. Theory 38, 719–746 (1992).
[CrossRef]

C.-I. Chang, “An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis,” IEEE Trans. Inf. Theory 46, 1927–1932(2000).
[CrossRef]

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

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989).
[CrossRef]

Lincoln Lab. J. (1)

D. Manolakis, D. Marden, and G. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J. 14, 79–114 (2003).

Opt. Eng. (3)

D. Manolakis, “Taxonomy of detection algorithms for hyperspectral imaging applications,” Opt. Eng. 44, 066403 (2005).
[CrossRef]

M. S. Alam and M. A. Karim, “Multiple target detection using a modified fringe-adjusted joint transform correlator,” Opt. Eng. 33, 1610–1617 (1994).
[CrossRef]

M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula, and A. Sharma, “Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[CrossRef]

Opt. Lett. (1)

Patt. Recogn. Lett. (1)

S. M. Yamany, A. A. Farag, and S.-Y. Hsu, “A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems,” Patt. Recogn. Lett. 20, 1431–1438 (1999).
[CrossRef]

Proc. SPIE (1)

W. Sakla, A. Sakla, and M. S. Alam, “Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation,” (invited paper) Proc. SPIE 6967, 69670B (2008).
[CrossRef]

Other (6)

C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (Kluwer Academic/Plenum, 2003).

A. K. Jain, Fundamentals of Digital Image Processing(Prentice-Hall, 1989).

M. Vetterli and J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, 1995).

S. Mallat, A Wavelet Tour of Signal Processing, 2nd. ed.(Academic, 1999).

ITRES Research, http://www.itres.com, accessed in 2007.

R. A. Schowengerdt, Remote Sensing, 2nd ed. (Academic, 1997).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (10)

Fig. 1
Fig. 1

Recursive filtering diagram of the Mallat algorithm for 1D DWT.

Fig. 2
Fig. 2

CASI urban (left) and vegetative (right) scenery.

Fig. 3
Fig. 3

HYDICE urban (left) and vegetative (right) scenery.

Fig. 4
Fig. 4

Binary truth image showing locations of inserted targets.

Fig. 5
Fig. 5

Targets inserted into CASI_urban_10 (left) and CASI_veg_10 (right) scenes.

Fig. 6
Fig. 6

Targets inserted into HYDICE_urban_10 (left) and HYDICE_veg_10 (right) scenes.

Fig. 7
Fig. 7

SFJTC ROC curve comparisons for CASI_urban_10.

Fig. 8
Fig. 8

SFJTC ROC curve comparisons for CASI_veg_10.

Fig. 9
Fig. 9

SFJTC ROC curve comparisons for HYDICE_urban_8.

Fig. 10
Fig. 10

SFJTC ROC curve comparisons for HYDICE_veg_8.

Tables (8)

Tables Icon

Table 1 DWT Coefficient Combinations for Decomposition Levels 1 to 3

Tables Icon

Table 2 Optimal DWT Coefficient Results on CASI Scenery

Tables Icon

Table 3 Optimal DWT Coefficient Results on HYDICE Scenery

Tables Icon

Table 4 SFJTC AUROC Comparisons for Urban CASI Scenery

Tables Icon

Table 5 SFJTC AUROC Comparisons for Vegetative CASI Scenery

Tables Icon

Table 6 SFJTC AUROC Comparisons for Urban HYDICE Scenery

Tables Icon

Table 7 SFJTC AUROC Comparisons for Vegetative HYDICE Scenery

Tables Icon

Table 8 Summary Statistics of SFJTC AUROC Comparisons

Equations (15)

Equations on this page are rendered with MathJax. Learn more.

f [ λ ] = r [ λ + c ] + s [ λ c ] .
F ( u ) = | R ( u ) | exp [ j ϕ r ( u ) ] exp ( j u c ) + | S ( u ) | exp [ j ϕ s ( u ) ] exp ( j u c ) ,
| F ( u ) | 2 = | R ( u ) | 2 + | S ( u ) | 2 + | R ( u ) S ( u ) | exp [ j { ϕ r ( u ) ϕ s ( u ) + 2 u c } ] + | R ( u ) S ( u ) | exp [ j { ϕ s ( u ) ϕ r ( u ) 2 u c } ] .
| I ( u ) | 2 = | F ( u ) | 2 | R ( u ) | 2 | S ( u ) | 2 = | R ( u ) S ( u ) | exp [ j { ϕ r ( u ) ϕ s ( u ) + 2 u c } ] + | R ( u ) S ( u ) | exp [ j { ϕ s ( u ) ϕ r ( u ) 2 u c } ] .
H ( u ) = A ( u ) B ( u ) + | R ( u ) | 2 ,
H ( u ) 1 | R ( u ) | 2 .
C ( x ) = F 1 { H ( u ) × | I ( u ) | 2 } .
s i , j [ λ ] = [ s i , j ( 1 ) , s i , j ( 2 ) , s i , j ( K ) ] T , i = 1 , 2 , , N R , j = 1 , 2 , , N C .
D = ( peak μ clutter ) α ,
μ clutter = 1 L C 1 j , j x peak C ( x ) ,
w = [ c A K c D K c D K 1 c D 1 ] .
x N K [ t , Γ ] .
Γ = σ 2 · R ,
R = [ 1 ρ ρ 2 ρ K 1 ρ 1 ρ ρ 2 ρ 2 ρ 1 ρ ρ 2 ρ 2 ρ 1 ρ ρ K 1 ρ 2 ρ 1 ] .
S N R = 1 K i = 1 K t i 2 σ .

Metrics