Abstract

We address method of detection of anomalies in hyperspectral images that consists in performing the detection when the spectral signatures of the targets are unknown. We show that, in real hyperspectral images, use of the full spectral resolution may not be necessary for detection but that the correlation properties of spectral fluctuations have to be taken into account in the design of the detection algorithm. Anomaly detectors are useful for detecting regions of interest (ROIs), but, as they are prone to false alarms, one must analyze the ROIs obtained further to decide whether they correspond to real targets. We propose a method of exploitation of these ROIs that consists in generating a single image in which the contrast of the ROI is optimized.

© 2006 Optical Society of America

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  1. J. C. Harsanyi and C. I. Chang, "Detection of low probability subpixel targets in hyperspectral image sequences with unknown backgrounds," IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994).
    [CrossRef]
  2. H. Ren and C. I. Chang, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images," Opt. Eng. 39, 3138-3145 (2000).
    [CrossRef]
  3. S. Kraut, L. L. Scharf, and L. T. McWorther, "An adaptive detection algorithm," IEEE Trans. Signal Process. 49, 1-16 (2001).
    [CrossRef]
  4. D. G. Manolakis and G. A. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag. 19, 29-43 (2002).
    [CrossRef]
  5. E. J. Kelly and K. M. Forsythe, "An adaptive detection algorithm," IEEE Trans. Aerosp. Electron. Syst. 22, 115-127 (1986).
  6. I. S. Reed and X. Yu, "Adaptive multiple band for detection of an optical pattern with unknown spectral distribution," IEEE Trans. Acoust. Speech Signal Process. 38, 1760-1770 (1990).
    [CrossRef]
  7. X. Yu, I. S. Reed, and A. D. Stocker, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41, 2639-2656 (1993).
    [CrossRef]
  8. S. M. Schweizer and J. M. F. Moura, "Efficient detection in hyperspectral imagery," IEEE Trans. Image Process. 10, 584-597 (2001).
    [CrossRef]
  9. D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
    [CrossRef]
  10. C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
    [CrossRef]
  11. D. G. Manolakis, G. A. Shaw, and N. Keshava, "Comparative analysis of hyperspectral adaptive matched filter detectors," in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S.S.Chen and M.R.Descour, eds., Proc. SPIE 4049, 2-17 (2000).
  12. M. Stefanou, "A signal processing perspective of hyperspectral imagery analysis techniques," Ph.D. dissertation (U.S. Naval Postgraduate School, 1997).
  13. C. G. Khatri and R. Rao, "Effects of estimated noise covariance matrix in optimal signal detection," IEEE Trans. Acoust. Speech Signal Process. 35, 671-679 (1987).
    [CrossRef]
  14. I. Kasen, P. A. Goa, and T. Skauli, "Target detection in hyperspectral images based on multicomponent statistical models for representation of background clutter," in Unmanned/Unattended Sensors and Sensor Networks, E.Carapezza, ed., Proc. SPIE 5612, 258-264 (2004).
  15. P. E. Goa, T. Skauli, I. Kasen, T. V. Haavardsholm, and A. Rodningsby, "Physical subspace models for invariant material identification: subspace composition and detection performance," in Optics in Atmospheric Propagation and Adaptive Systems VII, J.D.Gonglewski and K.Stein, eds., Proc. SPIE 5573, 203-214 (2004).
  16. S. M. Kay, "Statistical decision theory II," in Detection Theory, Vol. II of Fundamentals of Statistical Signal Processing (Prentice-Hall, 1998), pp. 186-247.
  17. H. V. Poor, "Elements of hypothesis testing," in An Introduction to Signal Detection and Estimation (Springer-Verlag, 1994).
  18. J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).
  19. P. E. Withagen, E. den Breejen, E. M. Franken, A. N. de Jong, and H. Winkel, "Band selection from a hyperspectral datacube for a real-time multispectral 3ccd camera," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 84-93 (2001).
  20. P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogram. Eng. Remote Sens. 70, 793-802 (2004).
  21. R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett. 2, 156-159 (2005).
    [CrossRef]
  22. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley, 1973).
  23. K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, 1990).

2005 (1)

R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett. 2, 156-159 (2005).
[CrossRef]

2004 (1)

P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogram. Eng. Remote Sens. 70, 793-802 (2004).

2002 (2)

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

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

2001 (2)

S. Kraut, L. L. Scharf, and L. T. McWorther, "An adaptive detection algorithm," IEEE Trans. Signal Process. 49, 1-16 (2001).
[CrossRef]

S. M. Schweizer and J. M. F. Moura, "Efficient detection in hyperspectral imagery," IEEE Trans. Image Process. 10, 584-597 (2001).
[CrossRef]

2000 (2)

H. Ren and C. I. Chang, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images," Opt. Eng. 39, 3138-3145 (2000).
[CrossRef]

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

1994 (1)

J. C. Harsanyi and C. I. Chang, "Detection of low probability subpixel targets in hyperspectral image sequences with unknown backgrounds," IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994).
[CrossRef]

1993 (1)

X. Yu, I. S. Reed, and A. D. Stocker, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41, 2639-2656 (1993).
[CrossRef]

1990 (1)

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

1987 (1)

C. G. Khatri and R. Rao, "Effects of estimated noise covariance matrix in optimal signal detection," IEEE Trans. Acoust. Speech Signal Process. 35, 671-679 (1987).
[CrossRef]

1986 (1)

E. J. Kelly and K. M. Forsythe, "An adaptive detection algorithm," IEEE Trans. Aerosp. Electron. Syst. 22, 115-127 (1986).

Bajcsy, P.

P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogram. Eng. Remote Sens. 70, 793-802 (2004).

Beaven, S. G.

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

Bucholtz, F.

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

Chang, C. I.

H. Ren and C. I. Chang, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images," Opt. Eng. 39, 3138-3145 (2000).
[CrossRef]

J. C. Harsanyi and C. I. Chang, "Detection of low probability subpixel targets in hyperspectral image sequences with unknown backgrounds," IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994).
[CrossRef]

de Jong, A. N.

P. E. Withagen, E. den Breejen, E. M. Franken, A. N. de Jong, and H. Winkel, "Band selection from a hyperspectral datacube for a real-time multispectral 3ccd camera," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 84-93 (2001).

den Breejen, E.

P. E. Withagen, E. den Breejen, E. M. Franken, A. N. de Jong, and H. Winkel, "Band selection from a hyperspectral datacube for a real-time multispectral 3ccd camera," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 84-93 (2001).

Duda, R. O.

R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley, 1973).

Forsythe, K. M.

E. J. Kelly and K. M. Forsythe, "An adaptive detection algorithm," IEEE Trans. Aerosp. Electron. Syst. 22, 115-127 (1986).

Fox, M. J.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Franken, E. M.

P. E. Withagen, E. den Breejen, E. M. Franken, A. N. de Jong, and H. Winkel, "Band selection from a hyperspectral datacube for a real-time multispectral 3ccd camera," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 84-93 (2001).

Fukunaga, K.

K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, 1990).

Goa, P. A.

I. Kasen, P. A. Goa, and T. Skauli, "Target detection in hyperspectral images based on multicomponent statistical models for representation of background clutter," in Unmanned/Unattended Sensors and Sensor Networks, E.Carapezza, ed., Proc. SPIE 5612, 258-264 (2004).

Goa, P. E.

P. E. Goa, T. Skauli, I. Kasen, T. V. Haavardsholm, and A. Rodningsby, "Physical subspace models for invariant material identification: subspace composition and detection performance," in Optics in Atmospheric Propagation and Adaptive Systems VII, J.D.Gonglewski and K.Stein, eds., Proc. SPIE 5573, 203-214 (2004).

Groves, P.

P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogram. Eng. Remote Sens. 70, 793-802 (2004).

Gruninger, J.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Haavardsholm, T. V.

P. E. Goa, T. Skauli, I. Kasen, T. V. Haavardsholm, and A. Rodningsby, "Physical subspace models for invariant material identification: subspace composition and detection performance," in Optics in Atmospheric Propagation and Adaptive Systems VII, J.D.Gonglewski and K.Stein, eds., Proc. SPIE 5573, 203-214 (2004).

Harsanyi, J. C.

J. C. Harsanyi and C. I. Chang, "Detection of low probability subpixel targets in hyperspectral image sequences with unknown backgrounds," IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994).
[CrossRef]

Hart, P. E.

R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley, 1973).

Hazel, G. G.

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

He, M.

R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett. 2, 156-159 (2005).
[CrossRef]

Hoff, L. E.

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

Huang, R.

R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett. 2, 156-159 (2005).
[CrossRef]

Kasen, I.

I. Kasen, P. A. Goa, and T. Skauli, "Target detection in hyperspectral images based on multicomponent statistical models for representation of background clutter," in Unmanned/Unattended Sensors and Sensor Networks, E.Carapezza, ed., Proc. SPIE 5612, 258-264 (2004).

P. E. Goa, T. Skauli, I. Kasen, T. V. Haavardsholm, and A. Rodningsby, "Physical subspace models for invariant material identification: subspace composition and detection performance," in Optics in Atmospheric Propagation and Adaptive Systems VII, J.D.Gonglewski and K.Stein, eds., Proc. SPIE 5573, 203-214 (2004).

Kay, S. M.

S. M. Kay, "Statistical decision theory II," in Detection Theory, Vol. II of Fundamentals of Statistical Signal Processing (Prentice-Hall, 1998), pp. 186-247.

Kelly, E. J.

E. J. Kelly and K. M. Forsythe, "An adaptive detection algorithm," IEEE Trans. Aerosp. Electron. Syst. 22, 115-127 (1986).

Keshava, N.

D. G. Manolakis, G. A. Shaw, and N. Keshava, "Comparative analysis of hyperspectral adaptive matched filter detectors," in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S.S.Chen and M.R.Descour, eds., Proc. SPIE 4049, 2-17 (2000).

Khatri, C. G.

C. G. Khatri and R. Rao, "Effects of estimated noise covariance matrix in optimal signal detection," IEEE Trans. Acoust. Speech Signal Process. 35, 671-679 (1987).
[CrossRef]

Kraut, S.

S. Kraut, L. L. Scharf, and L. T. McWorther, "An adaptive detection algorithm," IEEE Trans. Signal Process. 49, 1-16 (2001).
[CrossRef]

Levine, R.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Manolakis, D. G.

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

D. G. Manolakis, G. A. Shaw, and N. Keshava, "Comparative analysis of hyperspectral adaptive matched filter detectors," in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S.S.Chen and M.R.Descour, eds., Proc. SPIE 4049, 2-17 (2000).

McWorther, L. T.

S. Kraut, L. L. Scharf, and L. T. McWorther, "An adaptive detection algorithm," IEEE Trans. Signal Process. 49, 1-16 (2001).
[CrossRef]

Michalowicz, J. V.

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

Moura, J. M. F.

S. M. Schweizer and J. M. F. Moura, "Efficient detection in hyperspectral imagery," IEEE Trans. Image Process. 10, 584-597 (2001).
[CrossRef]

Mundkowsky, W. F.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Poor, H. V.

H. V. Poor, "Elements of hypothesis testing," in An Introduction to Signal Detection and Estimation (Springer-Verlag, 1994).

Rao, R.

C. G. Khatri and R. Rao, "Effects of estimated noise covariance matrix in optimal signal detection," IEEE Trans. Acoust. Speech Signal Process. 35, 671-679 (1987).
[CrossRef]

Ratcliff, A. H.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Reed, I. S.

X. Yu, I. S. Reed, and A. D. Stocker, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41, 2639-2656 (1993).
[CrossRef]

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

Ren, H.

H. Ren and C. I. Chang, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images," Opt. Eng. 39, 3138-3145 (2000).
[CrossRef]

Rodningsby, A.

P. E. Goa, T. Skauli, I. Kasen, T. V. Haavardsholm, and A. Rodningsby, "Physical subspace models for invariant material identification: subspace composition and detection performance," in Optics in Atmospheric Propagation and Adaptive Systems VII, J.D.Gonglewski and K.Stein, eds., Proc. SPIE 5573, 203-214 (2004).

Salisbury, M. S.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Schaaf, W.

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

Scharf, L. L.

S. Kraut, L. L. Scharf, and L. T. McWorther, "An adaptive detection algorithm," IEEE Trans. Signal Process. 49, 1-16 (2001).
[CrossRef]

Schaum, A. P.

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

Schweizer, S. M.

S. M. Schweizer and J. M. F. Moura, "Efficient detection in hyperspectral imagery," IEEE Trans. Image Process. 10, 584-597 (2001).
[CrossRef]

Shaw, G. A.

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

D. G. Manolakis, G. A. Shaw, and N. Keshava, "Comparative analysis of hyperspectral adaptive matched filter detectors," in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S.S.Chen and M.R.Descour, eds., Proc. SPIE 4049, 2-17 (2000).

Skauli, T.

P. E. Goa, T. Skauli, I. Kasen, T. V. Haavardsholm, and A. Rodningsby, "Physical subspace models for invariant material identification: subspace composition and detection performance," in Optics in Atmospheric Propagation and Adaptive Systems VII, J.D.Gonglewski and K.Stein, eds., Proc. SPIE 5573, 203-214 (2004).

I. Kasen, P. A. Goa, and T. Skauli, "Target detection in hyperspectral images based on multicomponent statistical models for representation of background clutter," in Unmanned/Unattended Sensors and Sensor Networks, E.Carapezza, ed., Proc. SPIE 5612, 258-264 (2004).

Stefanou, M.

M. Stefanou, "A signal processing perspective of hyperspectral imagery analysis techniques," Ph.D. dissertation (U.S. Naval Postgraduate School, 1997).

Stein, D. W. J.

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

Stellman, C. M.

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

Stocker, A. D.

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. D. Stocker, and W. Schaaf, "Real-time hyperspectral detection and cuing," Opt. Eng. 39, 1928-1935 (2000).
[CrossRef]

X. Yu, I. S. Reed, and A. D. Stocker, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41, 2639-2656 (1993).
[CrossRef]

Sundberg, R. L.

J. Gruninger, R. L. Sundberg, M. J. Fox, R. Levine, W. F. Mundkowsky, M. S. Salisbury, and A. H. Ratcliff, "Automated optimal channel selection for spectral imaging sensors," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 68-75 (2001).

Winkel, H.

P. E. Withagen, E. den Breejen, E. M. Franken, A. N. de Jong, and H. Winkel, "Band selection from a hyperspectral datacube for a real-time multispectral 3ccd camera," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 84-93 (2001).

Winter, E. M.

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

Withagen, P. E.

P. E. Withagen, E. den Breejen, E. M. Franken, A. N. de Jong, and H. Winkel, "Band selection from a hyperspectral datacube for a real-time multispectral 3ccd camera," in Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S.Shen and M.R.Descour, eds., Proc. SPIE 4381, 84-93 (2001).

Yu, X.

X. Yu, I. S. Reed, and A. D. Stocker, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41, 2639-2656 (1993).
[CrossRef]

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

IEEE Geosci. Remote Sens. Lett. (1)

R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett. 2, 156-159 (2005).
[CrossRef]

IEEE Signal Process. Mag. (2)

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

D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19, 58-69 (2002).
[CrossRef]

IEEE Trans. Acoust. Speech Signal Process. (2)

C. G. Khatri and R. Rao, "Effects of estimated noise covariance matrix in optimal signal detection," IEEE Trans. Acoust. Speech Signal Process. 35, 671-679 (1987).
[CrossRef]

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

IEEE Trans. Aerosp. Electron. Syst. (1)

E. J. Kelly and K. M. Forsythe, "An adaptive detection algorithm," IEEE Trans. Aerosp. Electron. Syst. 22, 115-127 (1986).

IEEE Trans. Geosci. Remote Sens. (1)

J. C. Harsanyi and C. I. Chang, "Detection of low probability subpixel targets in hyperspectral image sequences with unknown backgrounds," IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994).
[CrossRef]

IEEE Trans. Image Process. (1)

S. M. Schweizer and J. M. F. Moura, "Efficient detection in hyperspectral imagery," IEEE Trans. Image Process. 10, 584-597 (2001).
[CrossRef]

IEEE Trans. Signal Process. (2)

S. Kraut, L. L. Scharf, and L. T. McWorther, "An adaptive detection algorithm," IEEE Trans. Signal Process. 49, 1-16 (2001).
[CrossRef]

X. Yu, I. S. Reed, and A. D. Stocker, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41, 2639-2656 (1993).
[CrossRef]

Opt. Eng. (2)

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