Abstract

A novel spectral fringe-adjusted joint transform (SFJTC) correlation based technique is proposed for detecting very small targets involving only a few pixels in hyperspectral imagery. In this technique, spectral signatures from the unknown hyperspectral imagery are correlated with the reference signature using the SFJTC technique. This technique can detect both single and/or multiple desired targets in constant time while accommodating the in-plane and out-of-plane distortions. Furthermore, in this paper, a new metric, called the peak-to-clutter mean, is introduced that provides sharp and high correlation peaks corresponding to targets and makes the proposed technique intensity invariant. Test results using real life hyperspectral image datacubes are presented to verify the effectiveness of the proposed technique.

© 2010 Optical Society of America

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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
  16. M. S. Alam and S. Ochilov, “Target detection in hyperspectral imagery using one-dimensional fringe-adjusted joint transform correlation,” Proc. SPIE 6245, 624505 (2006).
    [CrossRef]
  17. 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]
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    [CrossRef]

2008 (1)

M. N. Islam and M. S. Alam, “Pattern recognition in hyperspectral imagery using 1D shifted phase-encoded joint transform correlation,” J. Opt. Commun. 281, 4854-4861 (2008).
[CrossRef]

2006 (1)

M. S. Alam and S. Ochilov, “Target detection in hyperspectral imagery using one-dimensional fringe-adjusted joint transform correlation,” Proc. SPIE 6245, 624505 (2006).
[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. R. Parker, S. C. Gustafson, and T. D. Ross, “Receiver operating characteristic and confidence error metrics for assessing the performance of automatic target recognition systems,” Opt. Eng. 44, 097202 (2005).
[CrossRef]

2004 (2)

M. S. Alam, M. Haque, J. F. Khan, and H. Kettani, “Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence,” Opt. Eng. 43, 1407-1413 (2004).
[CrossRef]

A. Mahalanobis, R. R. Muise, and S. R. Stanfill, “Quadratic correlation filter design methodology for target detection and surveillance applications,” Appl. Opt. 43, 5198-5205 (2004).
[CrossRef] [PubMed]

2003 (1)

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

2002 (1)

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

1999 (1)

D. Slater and G. Healey, “A spectral change space representation for invariant material tracking in hyperspectral images,” Proc. SPIE 3753, 308-317 (1999).
[CrossRef]

1994 (1)

1993 (1)

1990 (1)

I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. 38, 1760-1770(1990).
[CrossRef]

1986 (1)

1966 (1)

1936 (1)

R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugenics 7, 179-188 (1936).
[CrossRef]

Alam, M. S.

M. S. Alam, S. F. Goh, and S. Dacharaju, “Three-dimensional color pattern recognition using fringe-adjusted joint transform correlation with CIELab coordinates,” IEEE Trans. Instrum. Meas. (to be published).

M. N. Islam and M. S. Alam, “Pattern recognition in hyperspectral imagery using 1D shifted phase-encoded joint transform correlation,” J. Opt. Commun. 281, 4854-4861 (2008).
[CrossRef]

M. S. Alam and S. Ochilov, “Target detection in hyperspectral imagery using one-dimensional fringe-adjusted joint transform correlation,” Proc. SPIE 6245, 624505 (2006).
[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, M. Haque, J. F. Khan, and H. Kettani, “Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence,” Opt. Eng. 43, 1407-1413 (2004).
[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]

Dacharaju, S.

M. S. Alam, S. F. Goh, and S. Dacharaju, “Three-dimensional color pattern recognition using fringe-adjusted joint transform correlation with CIELab coordinates,” IEEE Trans. Instrum. Meas. (to be published).

Fisher, R. A.

R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugenics 7, 179-188 (1936).
[CrossRef]

Goh, S. F.

M. S. Alam, S. F. Goh, and S. Dacharaju, “Three-dimensional color pattern recognition using fringe-adjusted joint transform correlation with CIELab coordinates,” IEEE Trans. Instrum. Meas. (to be published).

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.

Gustafson, S. C.

D. R. Parker, S. C. Gustafson, and T. D. Ross, “Receiver operating characteristic and confidence error metrics for assessing the performance of automatic target recognition systems,” Opt. Eng. 44, 097202 (2005).
[CrossRef]

Haque, M.

M. S. Alam, M. Haque, J. F. Khan, and H. Kettani, “Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence,” Opt. Eng. 43, 1407-1413 (2004).
[CrossRef]

Healey, G.

D. Slater and G. Healey, “A spectral change space representation for invariant material tracking in hyperspectral images,” Proc. SPIE 3753, 308-317 (1999).
[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]

Islam, M. N.

M. N. Islam and M. S. Alam, “Pattern recognition in hyperspectral imagery using 1D shifted phase-encoded joint transform correlation,” J. Opt. Commun. 281, 4854-4861 (2008).
[CrossRef]

Javidi, B.

Karim, M. A.

Kay, S. M.

S. M. Kay, Fundamentals of Statistical Signal Processing (Prentice-Hall, 1998).

Kettani, H.

M. S. Alam, M. Haque, J. F. Khan, and H. Kettani, “Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence,” Opt. Eng. 43, 1407-1413 (2004).
[CrossRef]

Khan, J. F.

M. S. Alam, M. Haque, J. F. Khan, and H. Kettani, “Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence,” Opt. Eng. 43, 1407-1413 (2004).
[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]

Ludman, J. E.

Mahalanobis, A.

Manolakis, D.

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).

Muise, R. R.

Ochilov, S.

M. S. Alam and S. Ochilov, “Target detection in hyperspectral imagery using one-dimensional fringe-adjusted joint transform correlation,” Proc. SPIE 6245, 624505 (2006).
[CrossRef]

Parker, D. R.

D. R. Parker, S. C. Gustafson, and T. D. Ross, “Receiver operating characteristic and confidence error metrics for assessing the performance of automatic target recognition systems,” Opt. Eng. 44, 097202 (2005).
[CrossRef]

Reed, I. S.

I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. 38, 1760-1770(1990).
[CrossRef]

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]

Ross, T. D.

D. R. Parker, S. C. Gustafson, and T. D. Ross, “Receiver operating characteristic and confidence error metrics for assessing the performance of automatic target recognition systems,” Opt. Eng. 44, 097202 (2005).
[CrossRef]

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]

Slater, D.

D. Slater and G. Healey, “A spectral change space representation for invariant material tracking in hyperspectral images,” Proc. SPIE 3753, 308-317 (1999).
[CrossRef]

Stanfill, S. R.

Tang, Q.

Weaver, C. S.

Yu, F. T. S.

Yu, X.

I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. 38, 1760-1770(1990).
[CrossRef]

Ann. Eugenics (1)

R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugenics 7, 179-188 (1936).
[CrossRef]

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. Acoust. Speech Signal Process. (1)

I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. 38, 1760-1770(1990).
[CrossRef]

IEEE Trans. Instrum. Meas. (1)

M. S. Alam, S. F. Goh, and S. Dacharaju, “Three-dimensional color pattern recognition using fringe-adjusted joint transform correlation with CIELab coordinates,” IEEE Trans. Instrum. Meas. (to be published).

J. Opt. Commun. (1)

M. N. Islam and M. S. Alam, “Pattern recognition in hyperspectral imagery using 1D shifted phase-encoded joint transform correlation,” J. Opt. Commun. 281, 4854-4861 (2008).
[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)

M. S. Alam, M. Haque, J. F. Khan, and H. Kettani, “Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence,” Opt. Eng. 43, 1407-1413 (2004).
[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]

D. R. Parker, S. C. Gustafson, and T. D. Ross, “Receiver operating characteristic and confidence error metrics for assessing the performance of automatic target recognition systems,” Opt. Eng. 44, 097202 (2005).
[CrossRef]

Opt. Lett. (1)

Proc. SPIE (2)

M. S. Alam and S. Ochilov, “Target detection in hyperspectral imagery using one-dimensional fringe-adjusted joint transform correlation,” Proc. SPIE 6245, 624505 (2006).
[CrossRef]

D. Slater and G. Healey, “A spectral change space representation for invariant material tracking in hyperspectral images,” Proc. SPIE 3753, 308-317 (1999).
[CrossRef]

Other (2)

S. M. Kay, Fundamentals of Statistical Signal Processing (Prentice-Hall, 1998).

Center for the Study of Earth from Space (CSES), “The Spectral Image Processing System,” SIPS User's Guide (University of Colorado, 1992), Vol. 1.1.

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Figures (7)

Fig. 1
Fig. 1

SFJTC algorithm block diagram.

Fig. 2
Fig. 2

Correlation results with ordinary signals.

Fig. 3
Fig. 3

Correlation results with spectral signatures.

Fig. 4
Fig. 4

Performance of SFJTC using Dataset 1.

Fig. 5
Fig. 5

Performance of SFJTC using Dataset 2.

Fig. 6
Fig. 6

Comparison of RX, SAM, and Spectral FJTC algorithms with Dataset 1 using the ROC curve.

Fig. 7
Fig. 7

Comparison of RX, SAM, and Spectral FJTC algorithms with Dataset 2 using the ROC curve.

Tables (2)

Tables Icon

Table 1 Correlation Peak Intensity (CPI) and Peak-to-Clutter Mean (PCM) for the True and False Signals Shown in Fig. 2 Generated by the Spectral FJTC Technique

Tables Icon

Table 2 Correlation Peak Intensity (CPI) and Peak-to-Clutter Mean (PCM) for the Spectral Signatures Shown in Fig. 3 Generated by the Spectral FJTC and JTC Techniques

Equations (9)

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

f ( x ) = r ( x + x 0 ) + t ( x x 0 ) ,
F ( u ) = | R ( u ) | exp [ j ϕ r ( u ) ] exp ( j u x 0 ) + | T ( u ) | exp [ j ϕ t ( u ) ] exp ( j u x 0 ) ,
| F ( u ) | 2 = | R ( u ) | 2 + | T ( u ) | 2 + | R ( u ) | | T ( u ) | * exp [ j { ϕ r ( u ) ϕ t ( u ) + 2 u x 0 } ] + | R ( u ) | * | T ( u ) | exp [ j { ϕ t ( u ) ϕ r ( u ) 2 u x 0 } ] .
| I ( u ) | 2 = | F ( u ) | 2 | R ( u ) | 2 | T ( u ) | 2 = | R ( u ) | | T ( u ) | * exp [ j { ϕ r ( u ) ϕ t ( u ) + 2 u x 0 } ] + | R ( u ) | * | T ( u ) | exp [ j { ϕ t ( u ) ϕ r ( u ) 2 u x 0 } ] .
H ( u ) = A ( u ) B ( u ) + | R ( u ) | 2 ,
H ( u ) 1 | R ( u ) | 2 .
G ( u ) = H ( u ) × | I ( u ) | 2 .
c ( x ) = F 1 { H ( u ) × | I ( u ) | 2 } .
PCM = CPI 2 m i = 1 m 2 c ( i ) ,

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