Abstract

Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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2015 (1)

2014 (5)

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

C. Liu and J. Gu, “Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 86–98 (2014).
[Crossref]

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19, 010901 (2014).
[Crossref]

X. Lin, G. Wetzstein, Y. Liu, and Q. Dai, “Dual-coded compressive hyperspectral imaging,” Opt. Lett. 39, 2044–2047 (2014).
[Crossref] [PubMed]

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graphics (TOG) 33, 233 (2014).
[Crossref]

2013 (1)

N. Hagen and M. W. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52, 090901 (2013).
[Crossref]

2012 (2)

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

2011 (3)

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

D. V. Dinakarababu, D. R. Golish, and M. E. Gehm, “Adaptive feature specific spectroscopy for rapid chemical identification,” Opt. Express 19, 4595–4610 (2011).
[Crossref] [PubMed]

T. Arnold, M. De Biasio, and R. Leitner, “Near-infrared imaging spectroscopy for counterfeit drug detection,” Proc. SPIE 8032, 80320Y (2011),.
[Crossref]

2010 (3)

H. Abdi and L. J. Williams, “Principal component analysis,” Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010).
[Crossref]

D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
[Crossref] [PubMed]

A. Ibrahim, S. Tominaga, and T. Horiuchi, “Spectral imaging method for material classification and inspection of printed circuit boards,” Opt. Eng. 49, 057201 (2010).
[Crossref]

2009 (1)

2007 (1)

2006 (1)

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: Principles and applications,” Cytom. Part A 69A, 735–747 (2006).
[Crossref]

2004 (1)

M. Hinnrichs, J. O. Jensen, and G. McAnally, “Handheld hyperspectral imager for standoff detection of chemical and biological aerosols,” Proc. SPIE 5268, 67–78(2004).
[Crossref]

2003 (2)

C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International 18, 71–80 (2003).
[Crossref]

H.-H. K. Burke and Gary A. Shaw, “Spectral imaging for remote sensing,” Lincoln Lab. J. 14, 3–28 (2003).

2000 (1)

N. Gat, “Imaging spectroscopy using tunable filters: a review,” Proc. SPIE 4056, 50–64 (2000),.
[Crossref]

1987 (1)

S. A. Macenka and M. P. Chrisp, “Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Spectrometer Design and performance,” Proc. SPIE 0834, 32–43 (1987).
[Crossref]

Abdi, H.

H. Abdi and L. J. Williams, “Principal component analysis,” Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010).
[Crossref]

Alexander, J.

E. DuPont, D. Chambers, J. Alexander, and K. Alley, “A spatial-spectral classification approach of multispectral data for ground perspective materials,” in “IEEE International Conference on Systems, Man, and Cybernetics (SMC),” (2011), pp. 3125–3129.

Alley, K.

E. DuPont, D. Chambers, J. Alexander, and K. Alley, “A spatial-spectral classification approach of multispectral data for ground perspective materials,” in “IEEE International Conference on Systems, Man, and Cybernetics (SMC),” (2011), pp. 3125–3129.

Arce, G.

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

Arce, G. R.

Arguello, H.

H. Rueda, H. Arguello, and G. R. Arce, “DMD-based implementation of patterned optical filter arrays for compressive spectral imaging,” J. Opt. Soc. Am. A 32, 80–89 (2015).
[Crossref]

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

Arnold, T.

T. Arnold, M. De Biasio, and R. Leitner, “Near-infrared imaging spectroscopy for counterfeit drug detection,” Proc. SPIE 8032, 80320Y (2011),.
[Crossref]

Bakker, W. H.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Bearman, G.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Brady, D.

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

Brady, D. J.

Breitwieser, D.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Burke, H.-H. K.

H.-H. K. Burke and Gary A. Shaw, “Spectral imaging for remote sensing,” Lincoln Lab. J. 14, 3–28 (2003).

Carin, L.

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

Carranza, E. J. M.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Chambers, D.

E. DuPont, D. Chambers, J. Alexander, and K. Alley, “A spatial-spectral classification approach of multispectral data for ground perspective materials,” in “IEEE International Conference on Systems, Man, and Cybernetics (SMC),” (2011), pp. 3125–3129.

Chang, C.-I.

C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (SpringerUS, 2003).
[Crossref]

Choi, K.

Chrisp, M. P.

S. A. Macenka and M. P. Chrisp, “Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Spectrometer Design and performance,” Proc. SPIE 0834, 32–43 (1987).
[Crossref]

Cover, T. M.

T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley & Sons, 2012).

Dai, Q.

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graphics (TOG) 33, 233 (2014).
[Crossref]

X. Lin, G. Wetzstein, Y. Liu, and Q. Dai, “Dual-coded compressive hyperspectral imaging,” Opt. Lett. 39, 2044–2047 (2014).
[Crossref] [PubMed]

Davis, M. R.

C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International 18, 71–80 (2003).
[Crossref]

De Biasio, M.

T. Arnold, M. De Biasio, and R. Leitner, “Near-infrared imaging spectroscopy for counterfeit drug detection,” Proc. SPIE 8032, 80320Y (2011),.
[Crossref]

de Smeth, J. B.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Dinakarababu, D. V.

Dunlop, M.

M. Dunlop, P. K. Poon, D. R. Golish, E. Vera, and M. Gehm, “Calibration challenges and initial experimental demonstration of an adaptive, feature-specific spectral imaging classifier,” in “Imaging and Applied Optics,” (Optical Society of America, 2013), p. CW2C.3.
[Crossref]

M. Dunlop, P. Poon, E. Vera, and M. E. Gehm, “Experimental validation of the adaptive feature-specific spectral imaging classifier,” in “Frontiers in Optics 2014,” (Optical Society of America, 2014), p. FTh2B.5.

DuPont, E.

E. DuPont, D. Chambers, J. Alexander, and K. Alley, “A spatial-spectral classification approach of multispectral data for ground perspective materials,” in “IEEE International Conference on Systems, Man, and Cybernetics (SMC),” (2011), pp. 3125–3129.

Eismann, M. T.

M. T. Eismann, Hyperspectral Remote Sensing (SPIE, 2012).
[Crossref]

Even, D.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Everitt, J. H.

C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International 18, 71–80 (2003).
[Crossref]

Fei, B.

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19, 010901 (2014).
[Crossref]

France, F.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Garini, Y.

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: Principles and applications,” Cytom. Part A 69A, 735–747 (2006).
[Crossref]

Gat, N.

N. Gat, “Imaging spectroscopy using tunable filters: a review,” Proc. SPIE 4056, 50–64 (2000),.
[Crossref]

Gehm, M.

M. Dunlop, P. K. Poon, D. R. Golish, E. Vera, and M. Gehm, “Calibration challenges and initial experimental demonstration of an adaptive, feature-specific spectral imaging classifier,” in “Imaging and Applied Optics,” (Optical Society of America, 2013), p. CW2C.3.
[Crossref]

Gehm, M. E.

Golish, D. R.

D. V. Dinakarababu, D. R. Golish, and M. E. Gehm, “Adaptive feature specific spectroscopy for rapid chemical identification,” Opt. Express 19, 4595–4610 (2011).
[Crossref] [PubMed]

M. Dunlop, P. K. Poon, D. R. Golish, E. Vera, and M. Gehm, “Calibration challenges and initial experimental demonstration of an adaptive, feature-specific spectral imaging classifier,” in “Imaging and Applied Optics,” (Optical Society of America, 2013), p. CW2C.3.
[Crossref]

Gosetti, F.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Gu, J.

C. Liu and J. Gu, “Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 86–98 (2014).
[Crossref]

Hagen, N.

N. Hagen and M. W. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52, 090901 (2013).
[Crossref]

Hecker, C. A.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Hernández-Lobato, D.

D. Hernández-Lobato, V. Sharmanska, K. Kersting, C. H. Lampert, and N. Quadrianto, “Mind the Nuisance: Gaussian Process Classification using Privileged noise,” in “Advances in Neural Information Processing Systems,” (2014), pp. 837–845.

Hinnrichs, M.

M. Hinnrichs, J. O. Jensen, and G. McAnally, “Handheld hyperspectral imager for standoff detection of chemical and biological aerosols,” Proc. SPIE 5268, 67–78(2004).
[Crossref]

Ho, K.

L. Hong and K. Ho, “Classification of BPSK and QPSK signals with unknown signal level using the Bayes technique,” in “Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS’03,” (IEEE2003), vol. 4, pp. IV–1.

Hong, L.

L. Hong and K. Ho, “Classification of BPSK and QPSK signals with unknown signal level using the Bayes technique,” in “Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS’03,” (IEEE2003), vol. 4, pp. IV–1.

Horiuchi, T.

A. Ibrahim, S. Tominaga, and T. Horiuchi, “Spectral imaging method for material classification and inspection of printed circuit boards,” Opt. Eng. 49, 057201 (2010).
[Crossref]

Ibrahim, A.

A. Ibrahim, S. Tominaga, and T. Horiuchi, “Spectral imaging method for material classification and inspection of printed circuit boards,” Opt. Eng. 49, 057201 (2010).
[Crossref]

Jensen, J. O.

M. Hinnrichs, J. O. Jensen, and G. McAnally, “Handheld hyperspectral imager for standoff detection of chemical and biological aerosols,” Proc. SPIE 5268, 67–78(2004).
[Crossref]

John, R.

Kersting, K.

D. Hernández-Lobato, V. Sharmanska, K. Kersting, C. H. Lampert, and N. Quadrianto, “Mind the Nuisance: Gaussian Process Classification using Privileged noise,” in “Advances in Neural Information Processing Systems,” (2014), pp. 837–845.

Kittle, D.

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
[Crossref] [PubMed]

Kudenov, M. W.

N. Hagen and M. W. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52, 090901 (2013).
[Crossref]

Lampert, C. H.

D. Hernández-Lobato, V. Sharmanska, K. Kersting, C. H. Lampert, and N. Quadrianto, “Mind the Nuisance: Gaussian Process Classification using Privileged noise,” in “Advances in Neural Information Processing Systems,” (2014), pp. 837–845.

Leitner, R.

T. Arnold, M. De Biasio, and R. Leitner, “Near-infrared imaging spectroscopy for counterfeit drug detection,” Proc. SPIE 8032, 80320Y (2011),.
[Crossref]

Lin, X.

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graphics (TOG) 33, 233 (2014).
[Crossref]

X. Lin, G. Wetzstein, Y. Liu, and Q. Dai, “Dual-coded compressive hyperspectral imaging,” Opt. Lett. 39, 2044–2047 (2014).
[Crossref] [PubMed]

Liu, C.

C. Liu and J. Gu, “Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 86–98 (2014).
[Crossref]

Liu, Y.

X. Lin, G. Wetzstein, Y. Liu, and Q. Dai, “Dual-coded compressive hyperspectral imaging,” Opt. Lett. 39, 2044–2047 (2014).
[Crossref] [PubMed]

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graphics (TOG) 33, 233 (2014).
[Crossref]

Lu, G.

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19, 010901 (2014).
[Crossref]

Macenka, S. A.

S. A. Macenka and M. P. Chrisp, “Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Spectrometer Design and performance,” Proc. SPIE 0834, 32–43 (1987).
[Crossref]

Manfredi, M.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Mao, C.

C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International 18, 71–80 (2003).
[Crossref]

Marengo, E.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Mazzucco, E.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

McAnally, G.

M. Hinnrichs, J. O. Jensen, and G. McAnally, “Handheld hyperspectral imager for standoff detection of chemical and biological aerosols,” Proc. SPIE 5268, 67–78(2004).
[Crossref]

McNamara, G.

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: Principles and applications,” Cytom. Part A 69A, 735–747 (2006).
[Crossref]

Nakanishi, K.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Naungayan, J.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Noomen, M. F.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Pfister, W.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Pitsianis, N. P.

Poon, P.

M. Dunlop, P. Poon, E. Vera, and M. E. Gehm, “Experimental validation of the adaptive feature-specific spectral imaging classifier,” in “Frontiers in Optics 2014,” (Optical Society of America, 2014), p. FTh2B.5.

Poon, P. K.

M. Dunlop, P. K. Poon, D. R. Golish, E. Vera, and M. Gehm, “Calibration challenges and initial experimental demonstration of an adaptive, feature-specific spectral imaging classifier,” in “Imaging and Applied Optics,” (Optical Society of America, 2013), p. CW2C.3.
[Crossref]

Poor, H. V.

H. V. Poor, An Sntroduction to Signal Detection and Estimation (Springer Science & Business Media, 2013).

Quadrianto, N.

D. Hernández-Lobato, V. Sharmanska, K. Kersting, C. H. Lampert, and N. Quadrianto, “Mind the Nuisance: Gaussian Process Classification using Privileged noise,” in “Advances in Neural Information Processing Systems,” (2014), pp. 837–845.

Robotti, E.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Rueda, H.

Schulz, T. J.

Sharmanska, V.

D. Hernández-Lobato, V. Sharmanska, K. Kersting, C. H. Lampert, and N. Quadrianto, “Mind the Nuisance: Gaussian Process Classification using Privileged noise,” in “Advances in Neural Information Processing Systems,” (2014), pp. 837–845.

Shaw, Gary A.

H.-H. K. Burke and Gary A. Shaw, “Spectral imaging for remote sensing,” Lincoln Lab. J. 14, 3–28 (2003).

Shor, P.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Smith, W.

W. Smith, Modern Optical Engineering, 4th ed. (McGraw-Hill Education, 2007).

Sun, X.

Thomas, J. A.

T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley & Sons, 2012).

Tominaga, S.

A. Ibrahim, S. Tominaga, and T. Horiuchi, “Spectral imaging method for material classification and inspection of printed circuit boards,” Opt. Eng. 49, 057201 (2010).
[Crossref]

Van der Meer, F. D.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

van der Meijde, M.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Van der Werff, H. M.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

van Ruitenbeek, F. J.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Velasco, A.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Vera, E.

M. Dunlop, P. K. Poon, D. R. Golish, E. Vera, and M. Gehm, “Calibration challenges and initial experimental demonstration of an adaptive, feature-specific spectral imaging classifier,” in “Imaging and Applied Optics,” (Optical Society of America, 2013), p. CW2C.3.
[Crossref]

M. Dunlop, P. Poon, E. Vera, and M. E. Gehm, “Experimental validation of the adaptive feature-specific spectral imaging classifier,” in “Frontiers in Optics 2014,” (Optical Society of America, 2014), p. FTh2B.5.

Wagadarikar, A.

Wagadarikar, A. A.

Warren, C. P.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Wetzstein, G.

Willett, R. M.

Williams, L. J.

H. Abdi and L. J. Williams, “Principal component analysis,” Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010).
[Crossref]

Woldai, T.

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

Wolfe, W. L.

W. L. Wolfe, Introduction to imaging spectrometers, vol. v. TT 25. (SPIE, Bellingham, Wa, 1997).
[Crossref]

Wu, J.

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graphics (TOG) 33, 233 (2014).
[Crossref]

Yang, C.

C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International 18, 71–80 (2003).
[Crossref]

Yee, S.

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Young, I. T.

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: Principles and applications,” Cytom. Part A 69A, 735–747 (2006).
[Crossref]

Zerbinati, O.

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

ACM Trans. Graphics (TOG) (1)

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graphics (TOG) 33, 233 (2014).
[Crossref]

Analytica Chimica Acta (1)

E. Marengo, M. Manfredi, O. Zerbinati, E. Robotti, E. Mazzucco, F. Gosetti, G. Bearman, F. France, and P. Shor, “Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects,” Analytica Chimica Acta 706, 229–237 (2011).
[Crossref] [PubMed]

Appl. Opt. (1)

Cytom. Part A (1)

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: Principles and applications,” Cytom. Part A 69A, 735–747 (2006).
[Crossref]

Geocarto International (1)

C. Yang, J. H. Everitt, M. R. Davis, and C. Mao, “A CCD camera-based hyperspectral imaging system for stationary and airborne applications,” Geocarto International 18, 71–80 (2003).
[Crossref]

IEEE Sig. Process. Mag. (1)

G. Arce, D. Brady, L. Carin, H. Arguello, and D. Kittle, “An introduction to compressive coded aperture spectral imaging,” IEEE Sig. Process. Mag. 31, 105–115 (2014).
[Crossref]

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

C. Liu and J. Gu, “Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 86–98 (2014).
[Crossref]

Internat. J. Appl. Earth Observ. Geoinform. (1)

F. D. Van der Meer, H. M. Van der Werff, F. J. van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai,“Multi- and hyperspectral geologic remote sensing: A review,” Internat. J. Appl. Earth Observ. Geoinform. 14, 112–128 (2012).
[Crossref]

J. Biomed. Opt. (1)

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19, 010901 (2014).
[Crossref]

J. Opt. Soc. Am. A (1)

Lincoln Lab. J. (1)

H.-H. K. Burke and Gary A. Shaw, “Spectral imaging for remote sensing,” Lincoln Lab. J. 14, 3–28 (2003).

Opt. Eng. (3)

A. Ibrahim, S. Tominaga, and T. Horiuchi, “Spectral imaging method for material classification and inspection of printed circuit boards,” Opt. Eng. 49, 057201 (2010).
[Crossref]

N. Hagen and M. W. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52, 090901 (2013).
[Crossref]

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco, D. Breitwieser, S. Yee, and J. Naungayan, “Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications,” Opt. Eng. 51, 111720 (2012).
[Crossref]

Opt. Express (3)

Opt. Lett. (1)

Proc. SPIE (4)

S. A. Macenka and M. P. Chrisp, “Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Spectrometer Design and performance,” Proc. SPIE 0834, 32–43 (1987).
[Crossref]

N. Gat, “Imaging spectroscopy using tunable filters: a review,” Proc. SPIE 4056, 50–64 (2000),.
[Crossref]

M. Hinnrichs, J. O. Jensen, and G. McAnally, “Handheld hyperspectral imager for standoff detection of chemical and biological aerosols,” Proc. SPIE 5268, 67–78(2004).
[Crossref]

T. Arnold, M. De Biasio, and R. Leitner, “Near-infrared imaging spectroscopy for counterfeit drug detection,” Proc. SPIE 8032, 80320Y (2011),.
[Crossref]

Wiley Interdisciplinary Reviews: Computational Statistics (1)

H. Abdi and L. J. Williams, “Principal component analysis,” Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010).
[Crossref]

Other (12)

H. V. Poor, An Sntroduction to Signal Detection and Estimation (Springer Science & Business Media, 2013).

M. Dunlop, P. K. Poon, D. R. Golish, E. Vera, and M. Gehm, “Calibration challenges and initial experimental demonstration of an adaptive, feature-specific spectral imaging classifier,” in “Imaging and Applied Optics,” (Optical Society of America, 2013), p. CW2C.3.
[Crossref]

W. Smith, Modern Optical Engineering, 4th ed. (McGraw-Hill Education, 2007).

M. Dunlop, P. Poon, E. Vera, and M. E. Gehm, “Experimental validation of the adaptive feature-specific spectral imaging classifier,” in “Frontiers in Optics 2014,” (Optical Society of America, 2014), p. FTh2B.5.

D. Hernández-Lobato, V. Sharmanska, K. Kersting, C. H. Lampert, and N. Quadrianto, “Mind the Nuisance: Gaussian Process Classification using Privileged noise,” in “Advances in Neural Information Processing Systems,” (2014), pp. 837–845.

L. Hong and K. Ho, “Classification of BPSK and QPSK signals with unknown signal level using the Bayes technique,” in “Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS’03,” (IEEE2003), vol. 4, pp. IV–1.

T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley & Sons, 2012).

W. L. Wolfe, Introduction to imaging spectrometers, vol. v. TT 25. (SPIE, Bellingham, Wa, 1997).
[Crossref]

E. DuPont, D. Chambers, J. Alexander, and K. Alley, “A spatial-spectral classification approach of multispectral data for ground perspective materials,” in “IEEE International Conference on Systems, Man, and Cybernetics (SMC),” (2011), pp. 3125–3129.

NASA/JPL-Caltech, “Aviris:airborne visible / infrared imaging spectrometer,” (2014).

M. T. Eismann, Hyperspectral Remote Sensing (SPIE, 2012).
[Crossref]

C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (SpringerUS, 2003).
[Crossref]

Supplementary Material (1)

NameDescription
» Visualization 1: MP4 (2948 KB)      Video from AFSSIC experimental runs

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Figures (9)

Fig. 1
Fig. 1 Schematic of the AFSSI-C. Light from the source enters from the left, where the entrance optic develops the intermediate image plane prior to lens 1. The light is dispersed by the first diffractive grating, and imaged onto the DMD by the second lens. Shown here are three spectral channels, illustrating the different positions each channel has on the DMD, all from the same spatial location (experimental system has 38 spectral channels). The DMD directs light to the dump or lens 3 on a mirror-by-mirror basis. Collimated light from lens 3 is sent through a second grating, and finally imaged onto the detector by the 4th lens.
Fig. 2
Fig. 2 Visualization of datacube progression through the AFSSI-C system. (a) The input cube is (b) sheared by the first grating, (c) encoded at the DMD, and finally (d) spatially re-registered by the second grating.
Fig. 3
Fig. 3 Depiction of the pPCA (simple 2D example). (a) First principal component before a measurement has been made. All of the hypotheses are equiprobable, depicted here as all points having the same grayscale value. (b) After a measurement has been made: the darker points are more probable hypotheses, with the less probable hypotheses taking on lighter shades of gray. The first principal component has now been shifted to the direction of greatest variation in the weighted data.
Fig. 4
Fig. 4 Joint-pPCA schematic. (a) The pictorial illustration of the formation of X(k) from Equation(7). The hypothesis spectra (1) are centered around the mean (2), and given a weight based on their probabilities (3). (b) The joint-pPCA calculation involves the stacking of the 1, . . . , C − 1 elements of X i,j (k) for each location (n′, l′) along a row into a larger matrix uv (k). The final joint-pPCA scatter matrix is formed from (k) multiplied by its mean-centered transpose, to arrive at scatter matrix (k). The first principal component of this much larger scatter matrix is then in the direction of greatest variation in the joint-position data.
Fig. 5
Fig. 5 Photograph of the experimental system. The detector is high on the left; the DMD is in the background. The entrance optic is on a linear stage in the foreground
Fig. 6
Fig. 6 Left: The source used in the classification experiments and simulations; Right: The 4-class spectral library.
Fig. 7
Fig. 7 Frame from a movie (see Visualization 1) showing experimental results for three different levels of TSNR, over the course of 30 measurements. The left column is a depiction of the DMD code, center is the output from the detector, and the right is the classification decision at the current measurement.
Fig. 8
Fig. 8 Comparison of the AFSSI-C experimental system results to the simulation results for multiple TSNR levels by plotting the classification error versus measurement. Shown are repeated experiments of a 64× 64× 38 spectral datacube and a 4-class library.
Fig. 9
Fig. 9 Simulation comparing the classification performance for different measurements at TSNR = 0 for different systems: the AFSSI-C with designed features (joint pPCA), the AFSSI-C with random features, the traditional pushbroom imager, the traditional tunable filter imager, and the traditional whiskbroom imager. The input is a 64× 64× 38 spectral datacube with a 4-class library.

Equations (11)

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Γ n l = n l rect ( x Δ l , y Δ n ) rect ( x α ( λ λ c ) Δ l , y Δ n ) × T n l S 0 ( x , y ; λ ) d x d y d λ .
Γ n l ( λ = λ c ) = n l rect ( x Δ l , y Δ n ) rect ( x Δ l , y Δ n ) T n l I 0 ( x , y ) δ ( λ λ c ) d x d y d λ = n l δ l l δ n n T n l I n l = T n l I n l ,
Γ nl ( λ = λ c + Δ λ ) = l n rect ( x Δ l , y Δ n ) rect ( x Δ ( l + 1 ) , y Δ n ) × T n l I 0 ( x , y ) δ ( λ ( λ c + Δ λ ) ) d x d y d λ = n l δ l l δ n n T n ( l + 1 ) I nl = T n ( l 1 ) I nl
Γ nl = κ = 0 C 1 T n ( l + κ ) S n l κ ,
Γ n ( l 1 ) = κ = 0 C 1 T n ( l + 1 + κ ) S n ( l + 1 ) κ .
P ( h i | { m } k ) = P ( { m } k | h i ) P ( h i ) P ( { m } k ) .
Q ( k ) = r = 1 R P ( h r | { m } k ) ( s r s ¯ ) ( s r s ¯ ) T = X ( k ) X T ( k ) .
X i j ( k ) = P ( h j | { m } k ) ( s i j s ¯ i ) ,
s ¯ = r = 1 R P ( h r | { m } k ) s r .
Q ˜ ( k ) = r ˜ = 1 R ˜ ( x ˜ r ˜ ( k ) x ˜ ¯ ( k ) ) ( x ˜ r ˜ ( k ) x ˜ ¯ ( k ) ) T ,
TSNR = 10 log 10 ( d min σ n ) .

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