Abstract

Hyperspectral change detection offers a promising approach to detect objects and features of remotely sensed areas that are too difficult to find in single images, such as slight changes in land cover and the insertion, deletion, or movement of small objects, by exploiting subtle differences in the imagery over time. Methods for performing such change detection, however, must effectively maintain invariance to typically larger image-to-image changes in illumination and environmental conditions, as well as misregistration and viewing differences between image observations, while remaining sensitive to small differences in scene content. Previous research has established predictive algorithms to overcome such natural changes between images, and these approaches have recently been extended to deal with space-varying changes. The challenges to effective change detection, however, are often exacerbated in an airborne imaging geometry because of the limitations in control over flight conditions and geometry, and some of the recent change detection algorithms have not been demonstrated in an airborne setting. We describe the airborne implementation and relative performance of such methods. We specifically attempt to characterize the effects of spatial misregistration on change detection performance, the efficacy of class-conditional predictors in an airborne setting, and extensions to the change detection approach, including physically motivated shadow transition classifiers and matched change filtering based on in-scene atmospheric normalization.

© 2008 Optical Society of America

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    [CrossRef]

2008

M. T. Eismann, J. Meola, and R. C. Hardie, “Hyperspectral change detection in the presence of diurnal and seasonal variations,” IEEE Trans. Geosci. Remote Sen. 46, 237-249(2008).

J. Meola and M. Eismann, “Image misregistration effects on hyperspectral change detection,” Proc. SPIE 6966, 69660Y (2008).

J. Kerekes, “Receiver operating characteristic curve confidence intervals and regions,” IEEE Geosci. Remote Sens. Lett. 5, 251-255 (2008).

2007

J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, “Analysis of hyperspectral change detection as affected by vegetation and illumination variations,” Proc. SPIE 6565, 65651S (2007).

2005

B. Stevenson, et al., “Design and performance of the Civil Air Patrol ARCHER hyperspectral processing system,” Proc. SPIE 5806, 731-742 (2005).

L. S. Bernstein, , “Validation of the Quick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery,” Proc. SPIE 5806, 668-679 (2005).

R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process. 14, 294-307 (2005).

R. Cossu, S. Chaudhuri, and L. Bruzzone, “A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images,” IEEE Geosci. Remote Sens. Lett. 2, 352-356 (2005).

M. J. Carlotto, “A cluster-based approach for detecting man-made objects and changes in imagery,” IEEE Trans. Geosci. Remote Sens. 43, 374-387 (2005).

2004

A. Schaum and A. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proc. SPIE 5425, 77-90 (2004).

2003

R. Mayer, F. Bucholtz, and D. Scribner, “Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery,” IEEE Trans. Geosci. Remote Sens. 41, 1136-1142 (2003).

2002

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

N. Keshava and J. F. Mustard, “Spectral unmixing,” IEEE Signal Process. Mag. 44-57 (January 2002).
[CrossRef]

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

2001

G. G. Hazel, “Object-level change detection in spectral imagery,” IEEE Trans. Geosci. Remote Sens. 39, 553-561 (2001).

2000

M. J. Carlotto, “Nonlinear background estimation and change detection for wide area search,” Opt. Eng. 39, 1223-1229(2000).

1999

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

1997

N. A. Obuchowski, “Nonparametric analysis of clustered ROC curve data,” Biometrics 53, 567-578 (1997).
[CrossRef]

H. H. Song, “Analysis of correlated ROC areas in diagnostic testing,” Biometrics 53, 370-382 (1997).
[CrossRef]

1995

J. R. G. Townshend and C. O. Justice, “Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index,” Int. J. Remote Sens. 16, 2187-2195 (1995).

1993

P. Masson and W. Pieczynski, “SEM algorithm and unsupervised statistical segmentation of satellite images,” IEEE Trans. Geosci. Remote Sens. 31, 618-633 (1993).

1990

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

F. A. Kruse, K. S. Kierein-Young, and J. W. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83-92 (1990).

1989

A. Singh, “Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sens. 10, 989-1003 (1989).

1988

E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 44, 837-845 (1988).
[CrossRef]

1985

A. Margalit, I. S. Reed, and R. M. Gagliadri, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Sys, AE-21, 394-405 (1985).

1980

Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun. 2884-95 (1980).

Acharya, P. K.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Adler-Golden, S. M.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Al-Kofahi, O.

R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process. 14, 294-307 (2005).

Anderson, G. P.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Andra, S.

R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process. 14, 294-307 (2005).

Barnard, K. J.

J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, “Analysis of hyperspectral change detection as affected by vegetation and illumination variations,” Proc. SPIE 6565, 65651S (2007).

Berk, A.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Bernstein, L. S.

L. S. Bernstein, , “Validation of the Quick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery,” Proc. SPIE 5806, 668-679 (2005).

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Boardman, J. W.

F. A. Kruse, K. S. Kierein-Young, and J. W. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83-92 (1990).

Bruzzone, L.

R. Cossu, S. Chaudhuri, and L. Bruzzone, “A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images,” IEEE Geosci. Remote Sens. Lett. 2, 352-356 (2005).

Bucholtz, F.

R. Mayer, F. Bucholtz, and D. Scribner, “Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery,” IEEE Trans. Geosci. Remote Sens. 41, 1136-1142 (2003).

Buzo, A.

Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun. 2884-95 (1980).

Carlotto, M. J.

M. J. Carlotto, “A cluster-based approach for detecting man-made objects and changes in imagery,” IEEE Trans. Geosci. Remote Sens. 43, 374-387 (2005).

M. J. Carlotto, “Nonlinear background estimation and change detection for wide area search,” Opt. Eng. 39, 1223-1229(2000).

B. G. Lee, V. T. Tom, and M. J. Carlotto, “A signal-symbol approach to change detection,” in Proceedings of the 5th National Conference on Artificial Intelligence (Morgan Kaufmann, 1986), pp. 1138-1143.

Chaudhuri, S.

R. Cossu, S. Chaudhuri, and L. Bruzzone, “A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images,” IEEE Geosci. Remote Sens. Lett. 2, 352-356 (2005).

Chetwynd, J. H.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Clarke-Pearson, D. L.

E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 44, 837-845 (1988).
[CrossRef]

Cooley, T.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

Cossu, R.

R. Cossu, S. Chaudhuri, and L. Bruzzone, “A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images,” IEEE Geosci. Remote Sens. Lett. 2, 352-356 (2005).

DeLong, D. M.

E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 44, 837-845 (1988).
[CrossRef]

DeLong, E. R.

E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 44, 837-845 (1988).
[CrossRef]

Dothe, H.

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Eismann, M.

J. Meola and M. Eismann, “Image misregistration effects on hyperspectral change detection,” Proc. SPIE 6966, 69660Y (2008).

Eismann, M. T.

M. T. Eismann, J. Meola, and R. C. Hardie, “Hyperspectral change detection in the presence of diurnal and seasonal variations,” IEEE Trans. Geosci. Remote Sen. 46, 237-249(2008).

J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, “Analysis of hyperspectral change detection as affected by vegetation and illumination variations,” Proc. SPIE 6565, 65651S (2007).

M. T. Eismann, “Strategies for hyperspectral target detection in complex background environments,” in Proceedings of the IEEE Aerospace Conference (IEEE, 2006), .

Felde, G. W.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Gagliadri, R. M.

A. Margalit, I. S. Reed, and R. M. Gagliadri, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Sys, AE-21, 394-405 (1985).

Gardner, J. A.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Gray, R.

Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun. 2884-95 (1980).

Hardie, R. C.

M. T. Eismann, J. Meola, and R. C. Hardie, “Hyperspectral change detection in the presence of diurnal and seasonal variations,” IEEE Trans. Geosci. Remote Sen. 46, 237-249(2008).

J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, “Analysis of hyperspectral change detection as affected by vegetation and illumination variations,” Proc. SPIE 6565, 65651S (2007).

Hazel, G. G.

G. G. Hazel, “Object-level change detection in spectral imagery,” IEEE Trans. Geosci. Remote Sens. 39, 553-561 (2001).

Hoke, M. L.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Jeong, L. S.

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Justice, C. O.

J. R. G. Townshend and C. O. Justice, “Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index,” Int. J. Remote Sens. 16, 2187-2195 (1995).

Kerekes, J.

J. Kerekes, “Receiver operating characteristic curve confidence intervals and regions,” IEEE Geosci. Remote Sens. Lett. 5, 251-255 (2008).

Keshava, N.

N. Keshava and J. F. Mustard, “Spectral unmixing,” IEEE Signal Process. Mag. 44-57 (January 2002).
[CrossRef]

Kierein-Young, K. S.

F. A. Kruse, K. S. Kierein-Young, and J. W. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83-92 (1990).

Kruse, F. A.

F. A. Kruse, K. S. Kierein-Young, and J. W. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83-92 (1990).

Lee, B. G.

B. G. Lee, V. T. Tom, and M. J. Carlotto, “A signal-symbol approach to change detection,” in Proceedings of the 5th National Conference on Artificial Intelligence (Morgan Kaufmann, 1986), pp. 1138-1143.

Lewis, P.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

Linde, Y.

Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun. 2884-95 (1980).

Manolakis, D.

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

Margalit, A.

A. Margalit, I. S. Reed, and R. M. Gagliadri, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Sys, AE-21, 394-405 (1985).

Masson, P.

P. Masson and W. Pieczynski, “SEM algorithm and unsupervised statistical segmentation of satellite images,” IEEE Trans. Geosci. Remote Sens. 31, 618-633 (1993).

Matthew, M. W.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Mayer, R.

R. Mayer, F. Bucholtz, and D. Scribner, “Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery,” IEEE Trans. Geosci. Remote Sens. 41, 1136-1142 (2003).

McHugh, M.

A. Schaum and M. McHugh, Analytic Methods of Image Registration: Displacement Estimation and Resampling, Report 9298 (Naval Research Laboratory, 1991).

Mello, J.

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Meola, J.

J. Meola and M. Eismann, “Image misregistration effects on hyperspectral change detection,” Proc. SPIE 6966, 69660Y (2008).

M. T. Eismann, J. Meola, and R. C. Hardie, “Hyperspectral change detection in the presence of diurnal and seasonal variations,” IEEE Trans. Geosci. Remote Sen. 46, 237-249(2008).

J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, “Analysis of hyperspectral change detection as affected by vegetation and illumination variations,” Proc. SPIE 6565, 65651S (2007).

Miller, D.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

Mustard, J. F.

N. Keshava and J. F. Mustard, “Spectral unmixing,” IEEE Signal Process. Mag. 44-57 (January 2002).
[CrossRef]

Obuchowski, N. A.

N. A. Obuchowski, “Nonparametric analysis of clustered ROC curve data,” Biometrics 53, 567-578 (1997).
[CrossRef]

Pieczynski, W.

P. Masson and W. Pieczynski, “SEM algorithm and unsupervised statistical segmentation of satellite images,” IEEE Trans. Geosci. Remote Sens. 31, 618-633 (1993).

Pukall, B.

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Radke, R. J.

R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process. 14, 294-307 (2005).

Ratkowski, A. J.

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Reed, I. S.

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

A. Margalit, I. S. Reed, and R. M. Gagliadri, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Sys, AE-21, 394-405 (1985).

Richtsmeier, S. C.

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Roysam, B.

R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process. 14, 294-307 (2005).

Scharf, L. L.

L. L. Scharf, Statistical Signal Processing (Addison-Wesley, 1991).

Schaum, A.

A. Schaum and A. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proc. SPIE 5425, 77-90 (2004).

A. Schaum and A. Stocker, “Long-interval chronochrome target detection,” in Proceedings of the 1997 International Symposium on Spectral Sensing Research (ISSSR, 1998).

A. Schaum and M. McHugh, Analytic Methods of Image Registration: Displacement Estimation and Resampling, Report 9298 (Naval Research Laboratory, 1991).

A. Schaum, “Local covariance equalization of hyperspectral imagery: advantages and limitations for target detection,” Proceedings of the IEEE Aerospace Conference (IEEE, 2005).

A. Schaum and A. D. Stocker, “Linear chromodynamics models for hyperspectral target detection,” in Proceedings of the 2003 IEEE Aerospace Conference (IEEE, 2003), Vol. 4, p. 1879.

Schowengerdt, R. A.

R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing (Academic, 1997).

Scribner, D.

R. Mayer, F. Bucholtz, and D. Scribner, “Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery,” IEEE Trans. Geosci. Remote Sens. 41, 1136-1142 (2003).

Shaw, G.

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

Shewchuck, J. R.

J. R. Shewchuck, “Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator,” First Workshop on Applied Computational Geometry (Association for Computing Machinery, 1996), pp. 124-133.

Singh, A.

A. Singh, “Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sens. 10, 989-1003 (1989).

Song, H. H.

H. H. Song, “Analysis of correlated ROC areas in diagnostic testing,” Biometrics 53, 370-382 (1997).
[CrossRef]

Stevenson, B.

B. Stevenson, et al., “Design and performance of the Civil Air Patrol ARCHER hyperspectral processing system,” Proc. SPIE 5806, 731-742 (2005).

Stocker, A.

A. Schaum and A. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proc. SPIE 5425, 77-90 (2004).

A. Schaum and A. Stocker, “Long-interval chronochrome target detection,” in Proceedings of the 1997 International Symposium on Spectral Sensing Research (ISSSR, 1998).

Stocker, A. D.

A. Schaum and A. D. Stocker, “Linear chromodynamics models for hyperspectral target detection,” in Proceedings of the 2003 IEEE Aerospace Conference (IEEE, 2003), Vol. 4, p. 1879.

Tom, V. T.

B. G. Lee, V. T. Tom, and M. J. Carlotto, “A signal-symbol approach to change detection,” in Proceedings of the 5th National Conference on Artificial Intelligence (Morgan Kaufmann, 1986), pp. 1138-1143.

Townshend, J. R. G.

J. R. G. Townshend and C. O. Justice, “Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index,” Int. J. Remote Sens. 16, 2187-2195 (1995).

Yu, X.

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

Biometrics

N. A. Obuchowski, “Nonparametric analysis of clustered ROC curve data,” Biometrics 53, 567-578 (1997).
[CrossRef]

H. H. Song, “Analysis of correlated ROC areas in diagnostic testing,” Biometrics 53, 370-382 (1997).
[CrossRef]

E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 44, 837-845 (1988).
[CrossRef]

IEEE Geosci. Remote Sens. Lett.

R. Cossu, S. Chaudhuri, and L. Bruzzone, “A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images,” IEEE Geosci. Remote Sens. Lett. 2, 352-356 (2005).

J. Kerekes, “Receiver operating characteristic curve confidence intervals and regions,” IEEE Geosci. Remote Sens. Lett. 5, 251-255 (2008).

IEEE Signal Process. Mag.

N. Keshava and J. F. Mustard, “Spectral unmixing,” IEEE Signal Process. Mag. 44-57 (January 2002).
[CrossRef]

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

IEEE Trans. Acoust., Speech Signal Process.

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

IEEE Trans. Aerospace Electron. Sys,

A. Margalit, I. S. Reed, and R. M. Gagliadri, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Sys, AE-21, 394-405 (1985).

IEEE Trans. Commun.

Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun. 2884-95 (1980).

IEEE Trans. Geosci. Remote Sen.

M. T. Eismann, J. Meola, and R. C. Hardie, “Hyperspectral change detection in the presence of diurnal and seasonal variations,” IEEE Trans. Geosci. Remote Sen. 46, 237-249(2008).

IEEE Trans. Geosci. Remote Sens.

G. G. Hazel, “Object-level change detection in spectral imagery,” IEEE Trans. Geosci. Remote Sens. 39, 553-561 (2001).

M. J. Carlotto, “A cluster-based approach for detecting man-made objects and changes in imagery,” IEEE Trans. Geosci. Remote Sens. 43, 374-387 (2005).

R. Mayer, F. Bucholtz, and D. Scribner, “Object detection by using whitening/dewhitening to transform target signatures in multitemporal hyperspectral and multispectral imagery,” IEEE Trans. Geosci. Remote Sens. 41, 1136-1142 (2003).

P. Masson and W. Pieczynski, “SEM algorithm and unsupervised statistical segmentation of satellite images,” IEEE Trans. Geosci. Remote Sens. 31, 618-633 (1993).

IEEE Trans. Image Process.

R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process. 14, 294-307 (2005).

Int. J. Remote Sens.

J. R. G. Townshend and C. O. Justice, “Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index,” Int. J. Remote Sens. 16, 2187-2195 (1995).

A. Singh, “Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sens. 10, 989-1003 (1989).

Opt. Eng.

M. J. Carlotto, “Nonlinear background estimation and change detection for wide area search,” Opt. Eng. 39, 1223-1229(2000).

Photogramm. Eng. Remote Sens.

F. A. Kruse, K. S. Kierein-Young, and J. W. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83-92 (1990).

Proc. SPIE

J. Meola and M. Eismann, “Image misregistration effects on hyperspectral change detection,” Proc. SPIE 6966, 69660Y (2008).

L. S. Bernstein, , “Validation of the Quick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery,” Proc. SPIE 5806, 668-679 (2005).

J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, “Analysis of hyperspectral change detection as affected by vegetation and illumination variations,” Proc. SPIE 6565, 65651S (2007).

B. Stevenson, et al., “Design and performance of the Civil Air Patrol ARCHER hyperspectral processing system,” Proc. SPIE 5806, 731-742 (2005).

A. Schaum and A. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proc. SPIE 5425, 77-90 (2004).

G. P. Anderson, G. W. Felde, M. L. Hoke, A. J. Ratkowski, T. Cooley, J. H. Chetwynd, J. A. Gardner, S. M. Adler-Golden, M. W. Matthew, A. Berk, L. S. Bernstein, P. K. Acharya, D. Miller, and P. Lewis, “MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes,” Proc. SPIE 4725, 65-71 (2002).

G. P. Anderson, A. Berk, P. K. Acharya, M. W. Matthew, L. S. Bernstein, J. H. Chetwynd, H. Dothe, S. M. Adler-Golden, A. J. Ratkowski, G. W. Felde, J. A. Gardner, M. L. Hoke, S. C. Richtsmeier, B. Pukall, J. Mello, and L. S. Jeong, “MODTRAN4: Radiative transfer modeling for remote sensing,” Proc. SPIE 3866, 2-10 (1999).

Other

A. Schaum and A. Stocker, “Long-interval chronochrome target detection,” in Proceedings of the 1997 International Symposium on Spectral Sensing Research (ISSSR, 1998).

A. Schaum and A. D. Stocker, “Linear chromodynamics models for hyperspectral target detection,” in Proceedings of the 2003 IEEE Aerospace Conference (IEEE, 2003), Vol. 4, p. 1879.

B. G. Lee, V. T. Tom, and M. J. Carlotto, “A signal-symbol approach to change detection,” in Proceedings of the 5th National Conference on Artificial Intelligence (Morgan Kaufmann, 1986), pp. 1138-1143.

R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing (Academic, 1997).

J. R. Shewchuck, “Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator,” First Workshop on Applied Computational Geometry (Association for Computing Machinery, 1996), pp. 124-133.

A. Schaum and M. McHugh, Analytic Methods of Image Registration: Displacement Estimation and Resampling, Report 9298 (Naval Research Laboratory, 1991).

L. L. Scharf, Statistical Signal Processing (Addison-Wesley, 1991).

A. Schaum, “Local covariance equalization of hyperspectral imagery: advantages and limitations for target detection,” Proceedings of the IEEE Aerospace Conference (IEEE, 2005).

M. T. Eismann, “Strategies for hyperspectral target detection in complex background environments,” in Proceedings of the IEEE Aerospace Conference (IEEE, 2006), .

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Figures (18)

Fig. 1
Fig. 1

Change detection block diagram.

Fig. 2
Fig. 2

Example photograph of ground-based change pair collected one month apart with man-made target insertions denoted by circles: (a) reference image and (b) test image.

Fig. 3
Fig. 3

Coregistered ARCHER hyperspectral images collected two days apart over a natural background area with man-made change target insertions denoted by red boxes: (a) reference image and (b) test image.

Fig. 4
Fig. 4

Coregistered ARCHER hyperspectral images collected two days apart with a 4 h time difference over the Mojave, California airport with inserted targets designated by arrows and target substitutions designated by circles: (a) reference image and (b) test image.

Fig. 5
Fig. 5

Effect of misregistration on the mean squared covariance matrix error for ground data simulation: cross-covariance matrix for chronochrome and test covariance matrix for covariance equalization.

Fig. 6
Fig. 6

Effect of misregistration on the ROC area under the curve (AUC) metric for ground data simulation comparing CC and CE: (a) misregistration only in the prediction step and (b) misregistration in both the prediction and subtraction steps.

Fig. 7
Fig. 7

Red band normalized difference of ARCHER images (a) before and (b) after scene-based registration processing.

Fig. 8
Fig. 8

Anomaly change detection statistic images for global predictors: (a) chronochrome and (b) covariance equalization.

Fig. 9
Fig. 9

ROC performance for global predictors.

Fig. 10
Fig. 10

Anomaly change detection statistic images for class-conditional predictors using reference image clustering: (a) chronochrome and (b) covariance equalization.

Fig. 11
Fig. 11

ROC performance for class-conditional predictors using reference image clustering.

Fig. 12
Fig. 12

Anomaly change detection statistic images for class-conditional predictors using joint image clustering: (a) chronochrome and (b) covariance equalization.

Fig. 13
Fig. 13

ROC performance for class-conditional predictors using joint image clustering.

Fig. 14
Fig. 14

Magnified section of anomaly change detection statistic images for class-conditional predictors using joint image clustering indicating differences in residual clutter: (a) chronochrome and (b) covariance equalization.

Fig. 15
Fig. 15

Root spectral eigenvalue distributions for ARCHER test image and class-conditional prediction residuals using joint image clustering.

Fig. 16
Fig. 16

Spectra of blue target change target: (a) in-scene ARCHER spectrum and (b) QUAC and vegetation normalized spectrum compared to ground truth.

Fig. 17
Fig. 17

Classification map of ARCHER Mojave image based on preliminary classification for shadow transitions (white and black) followed by three-class normal mixture classification of the joint image by SEM (colored areas).

Fig. 18
Fig. 18

Signature-matched single frame and change detection results using the QUAC normalized blue tarp signature and the ARCHER Mojave image: (a) single frame detection, (b) change detection with global CE predictor, (c) change detection with class-conditional CE predictor, and (d) change detection with class-conditional CC predictor.

Tables (2)

Tables Icon

Table 1 Ground-Based Spectrometer Specification

Tables Icon

Table 2 CAP ARCHER Sensor Specification

Equations (36)

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

I d ( λ ) = 0 2 π 0 π / 2 L d ( λ , θ , ϕ ) sin θ cos θ d θ d φ .
L p ( λ ) = τ a ( λ ) ρ ( λ ) π [ I s ( λ ) + I d ( λ ) ] + L a ( λ ) .
x ( λ ) = T ( λ ) ρ ( λ ) + d ( λ ) ,
T ( λ ) = g ( λ ) τ a ( λ ) π [ I s ( λ ) + I d ( λ ) ] ,
d ( λ ) = g ( λ ) L a ( λ ) + o ( λ ) .
X = TR + D + N ,
X = T x R + D x + N x ,
Y = T y R + D y + N y ,
X = T xy Y + D xy ,
T xy = T x T y 1 ,
D xy = D x T xy D y .
E = X ( T xy Y + D xy ) ,
E = T x Δ R + N x T xy N y ,
ε i = x i ( T xy y i d xy ) = T x Δ ρ i + n x T xy n y ,
m x = T x m ρ + d x ,
m y = T y m ρ + d y ,
C xx = T x C ρ ρ T x T + C n ,
C yy = T y C ρ ρ T y T + C n ,
C xy = T x C ρ ρ T y T + C n .
T ^ xy ( CE ) = C ^ xx 1 / 2 C ^ yy 1 / 2 ,
d ^ xy ( CE ) = m ^ x T ^ xy m ^ y .
T ^ xy ( CE ) = V x Σ x 1 / 2 V x T V y Σ y 1 / 2 V y T ,
T ^ xy ( CC ) = C ^ xy C ^ yy 1 .
m x ^ = T ^ xy m y + d ^ xy ,
C x ^ x ^ = T ^ xy C yy T ^ xy T .
m ε = m x T ^ xy m y d ^ xy ,
C ε ε = C xx T ^ xy C xy T C xy T ^ xy T + T ^ xy C yy T ^ xy T + C n + T ^ xy C n T ^ xy T .
C ε ε = C n + C xy C yy 1 C n C yy 1 C xy T .
C ε ε = 2 C xx C xx 1 / 2 C yy 1 / 2 C xy T C xy ( C yy 1 / 2 ) T ( C xx 1 / 2 ) T + C n + C xx 1 / 2 C yy 1 / 2 C n ( C yy 1 / 2 ) T ( C xx 1 / 2 ) T ,
x ^ i | q = ( T ^ xy | q ) y i + ( d ^ xy | q )
ε i = x i x ^ i = x i ( T ^ xy y i d ^ xy ) ,
r i = ε i T C ^ ε ε 1 ε i ,
C ^ ε ε = 1 N i ε i ε i T = 1 N EE T .
r i = s x T C ^ εε 1 ε i s x T C ^ εε 1 s x .
C ^ εε q = 1 Ω q i Ω q ε i ε i T ,
δ = 1 K 2 i = 1 K j = 1 K ( C [ i , j ] C ^ [ i , j ] ) 2 ,

Metrics