Abstract

By illumination of target scenes using a set of different wavelengths, we demonstrate color classification of scenes, as well as depth estimation, in photon-starved images. The spectral signatures are classified with a new advanced statistical image processing method from measurements of the same scene, in this case using combinations of 33, 16, 8 or 4 different wavelengths in the range 500 – 820 nm. This approach makes it possible to perform color classification and depth estimation on images containing as few as one photon per pixel, on average. Compared to single wavelength imaging, this approach improves target discrimination by extracting more spectral information, which, in turn, improves the depth estimation since this approach is robust to changes in target reflectivity. We demonstrate color classification and depth profiling of complex targets at average signal levels as low as 1.0 photons per pixel from as few as 4 different wavelength measurements.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. and Remote Sensing 54, 1349–1362 (2016).
    [Crossref]
  2. P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).
  3. H. Xie, J. Bec, J. Liu, Y. Sun, M. Lam, D. R. Yankelevich, and L. Marcu, “Multispectral scanning time-resolved fluorescence spectroscopy (trfs) technique for intravascular diagnosis,” Biomed. Opt. Express 3, 1521–1533 (2012).
    [Crossref] [PubMed]
  4. L. Marcu, “Fluorescence lifetime techniques in medical applications,” Ann. Biomed. Eng. 40, 304–331 (2012).
    [Crossref] [PubMed]
  5. G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
    [Crossref]
  6. A. M. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, “Single-photon three-dimensional imaging at up to 10 kilometers range,” Opt. Express 25, 11919–11931 (2017).
    [Crossref] [PubMed]
  7. J. J. Degnan and C. T. Field, “Moderate to high altitude, single photon sensitive, 3D imaging lidars,” Proc. SPIE 9114, Advanced Photon Counting Techniques VIII, 91140H (May 28, 2014).
  8. D. Alley, B. Cochenour, and L. Mullen, “Remotely operated compact underwater temporally encoded imager: Cutei,” Proc. SPIE 9827, 982708 (2016).
    [Crossref]
  9. A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an InGaAs/InP single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
    [Crossref] [PubMed]
  10. A. Maccarone, A. McCarthy, X. Ren, R. E. Warburton, A. M. Wallace, J. Moffat, Y. Petillot, and G. S. Buller, “Underwater depth imaging using time-correlated single-photon counting,” Opt. Express 23, 33911–33926 (2015).
    [Crossref]
  11. A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
    [Crossref]
  12. D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
    [Crossref]
  13. Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
    [Crossref] [PubMed]
  14. A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
    [Crossref]
  15. Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).
  16. Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).
  17. Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).
  18. Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
    [Crossref]
  19. Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).
  20. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).
    [Crossref]
  21. A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).
    [Crossref]
  22. M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
    [Crossref] [PubMed]
  23. M. Pereyra, N. Whiteley, C. Andrieu, and J.-Y. Tourneret, “Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Gold Coast, Australia, 2014).
  24. I. Jolliffe, Principal Component Analysis, Springer Series in Statistics (Springer, 2002).

2017 (2)

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

A. M. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, “Single-photon three-dimensional imaging at up to 10 kilometers range,” Opt. Express 25, 11919–11931 (2017).
[Crossref] [PubMed]

2016 (4)

D. Alley, B. Cochenour, and L. Mullen, “Remotely operated compact underwater temporally encoded imager: Cutei,” Proc. SPIE 9827, 982708 (2016).
[Crossref]

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. and Remote Sensing 54, 1349–1362 (2016).
[Crossref]

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

2015 (1)

2014 (1)

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

2013 (2)

M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
[Crossref] [PubMed]

A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an InGaAs/InP single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
[Crossref] [PubMed]

2012 (2)

2005 (1)

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

2004 (1)

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).
[Crossref]

1992 (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).
[Crossref]

Alley, D.

D. Alley, B. Cochenour, and L. Mullen, “Remotely operated compact underwater temporally encoded imager: Cutei,” Proc. SPIE 9827, 982708 (2016).
[Crossref]

Altmann, Y.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).

Andrieu, C.

M. Pereyra, N. Whiteley, C. Andrieu, and J.-Y. Tourneret, “Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Gold Coast, Australia, 2014).

Batatia, H.

M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
[Crossref] [PubMed]

Bec, J.

Buller, G.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).

Buller, G. S.

A. M. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, “Single-photon three-dimensional imaging at up to 10 kilometers range,” Opt. Express 25, 11919–11931 (2017).
[Crossref] [PubMed]

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

A. Maccarone, A. McCarthy, X. Ren, R. E. Warburton, A. M. Wallace, J. Moffat, Y. Petillot, and G. S. Buller, “Underwater depth imaging using time-correlated single-photon counting,” Opt. Express 23, 33911–33926 (2015).
[Crossref]

A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an InGaAs/InP single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
[Crossref] [PubMed]

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
[Crossref]

Camps-Valls, G.

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. and Remote Sensing 54, 1349–1362 (2016).
[Crossref]

Chambolle, A.

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).
[Crossref]

Chhabra, P.

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Cochenour, B.

D. Alley, B. Cochenour, and L. Mullen, “Remotely operated compact underwater temporally encoded imager: Cutei,” Proc. SPIE 9827, 982708 (2016).
[Crossref]

Colaço, A.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Degnan, J. J.

J. J. Degnan and C. T. Field, “Moderate to high altitude, single photon sensitive, 3D imaging lidars,” Proc. SPIE 9114, Advanced Photon Counting Techniques VIII, 91140H (May 28, 2014).

Dobigeon, N.

M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
[Crossref] [PubMed]

Fatemi, E.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).
[Crossref]

Field, C. T.

J. J. Degnan and C. T. Field, “Moderate to high altitude, single photon sensitive, 3D imaging lidars,” Proc. SPIE 9114, Advanced Photon Counting Techniques VIII, 91140H (May 28, 2014).

Frera, A. D.

Gatta, C.

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. and Remote Sensing 54, 1349–1362 (2016).
[Crossref]

Gemmell, N. R.

Goyal, V. K.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Halimi, A.

A. M. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, “Single-photon three-dimensional imaging at up to 10 kilometers range,” Opt. Express 25, 11919–11931 (2017).
[Crossref] [PubMed]

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
[Crossref]

Harkins, R. D.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Hero, A.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

Hiskett, P. A.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Jolliffe, I.

I. Jolliffe, Principal Component Analysis, Springer Series in Statistics (Springer, 2002).

Kirmani, A.

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Krichel, N. J.

Lam, M.

Lamb, R. A.

A. M. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, “Single-photon three-dimensional imaging at up to 10 kilometers range,” Opt. Express 25, 11919–11931 (2017).
[Crossref] [PubMed]

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Liu, J.

Lussana, R.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

Maccarone, A.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

A. Maccarone, A. McCarthy, X. Ren, R. E. Warburton, A. M. Wallace, J. Moffat, Y. Petillot, and G. S. Buller, “Underwater depth imaging using time-correlated single-photon counting,” Opt. Express 23, 33911–33926 (2015).
[Crossref]

A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
[Crossref]

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).

MacKinnon, G. R.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Marcu, L.

McCarthy, A.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

A. Maccarone, A. McCarthy, X. Ren, R. E. Warburton, A. M. Wallace, J. Moffat, Y. Petillot, and G. S. Buller, “Underwater depth imaging using time-correlated single-photon counting,” Opt. Express 23, 33911–33926 (2015).
[Crossref]

A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an InGaAs/InP single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
[Crossref] [PubMed]

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
[Crossref]

McLaughlin, S.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).

A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
[Crossref]

Moffat, J.

Mullen, L.

D. Alley, B. Cochenour, and L. Mullen, “Remotely operated compact underwater temporally encoded imager: Cutei,” Proc. SPIE 9827, 982708 (2016).
[Crossref]

Newstadt, G.

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

Osher, S.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).
[Crossref]

Pawlikowska, A. M.

Pereyra, M.

M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
[Crossref] [PubMed]

M. Pereyra, N. Whiteley, C. Andrieu, and J.-Y. Tourneret, “Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Gold Coast, Australia, 2014).

Petillot, Y.

Rarity, J. G.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Ren, X.

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

A. Maccarone, A. McCarthy, X. Ren, R. E. Warburton, A. M. Wallace, J. Moffat, Y. Petillot, and G. S. Buller, “Underwater depth imaging using time-correlated single-photon counting,” Opt. Express 23, 33911–33926 (2015).
[Crossref]

A. McCarthy, X. Ren, A. D. Frera, N. R. Gemmell, N. J. Krichel, C. Scarcella, A. Ruggeri, A. Tosi, and G. S. Buller, “Kilometer-range depth imaging at 1550 nm wavelength using an InGaAs/InP single-photon avalanche diode detector,” Opt. Express 21, 22098–22113 (2013).
[Crossref] [PubMed]

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

Ridley, K. D.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Romero, A.

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. and Remote Sensing 54, 1349–1362 (2016).
[Crossref]

Rudin, L. I.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).
[Crossref]

Ruggeri, A.

Scarcella, C.

Shapiro, J. H.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Shin, D.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Smith, G. R.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Sun, Y.

Sung, R.

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Tobin, R.

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

Tosi, A.

Tourneret, J.-Y.

M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
[Crossref] [PubMed]

M. Pereyra, N. Whiteley, C. Andrieu, and J.-Y. Tourneret, “Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Gold Coast, Australia, 2014).

Venkatraman, D.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Villa, F.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

Wallace, A.

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

Wallace, A. M.

A. Maccarone, A. McCarthy, X. Ren, R. E. Warburton, A. M. Wallace, J. Moffat, Y. Petillot, and G. S. Buller, “Underwater depth imaging using time-correlated single-photon counting,” Opt. Express 23, 33911–33926 (2015).
[Crossref]

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Warburton, R. E.

Whiteley, N.

M. Pereyra, N. Whiteley, C. Andrieu, and J.-Y. Tourneret, “Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Gold Coast, Australia, 2014).

Wong, F. N. C.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Xie, H.

Xu, F.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

Yankelevich, D. R.

Zappa, F.

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

Ann. Biomed. Eng. (1)

L. Marcu, “Fluorescence lifetime techniques in medical applications,” Ann. Biomed. Eng. 40, 304–331 (2012).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

IEEE Trans. Comput. Imaging (1)

Y. Altmann, A. Maccarone, A. McCarthy, G. Newstadt, G. Buller, S. McLaughlin, and A. Hero, “Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields,” IEEE Trans. Comput. Imaging 3, 658–670 (2017).
[Crossref]

IEEE Trans. Geosci. and Remote Sensing (1)

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. and Remote Sensing 54, 1349–1362 (2016).
[Crossref]

IEEE Trans. Image Process. (2)

Y. Altmann, X. Ren, A. McCarthy, G. S. Buller, and S. McLaughlin, “Lidar waveform based analysis of depth images constructed using sparse single-photon data,” IEEE Trans. Image Process. 25, 1935–1946 (2016).
[Crossref] [PubMed]

M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, “Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” IEEE Trans. Image Process. 22, 2385–2397 (2013).
[Crossref] [PubMed]

J. Math. Imaging Vision (1)

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).
[Crossref]

Nat. Communications (1)

D. Shin, F. Xu, D. Venkatraman, R. Lussana, F. Villa, F. Zappa, V. K. Goyal, F. N. C. Wong, and J. H. Shapiro, “Photon-efficient imaging with a single-photon camera,” Nat. Communications 7, 12046 (2016).
[Crossref]

Opt. Express (3)

Phys. D (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).
[Crossref]

Proc. SPIE (1)

D. Alley, B. Cochenour, and L. Mullen, “Remotely operated compact underwater temporally encoded imager: Cutei,” Proc. SPIE 9827, 982708 (2016).
[Crossref]

Rev. Sci. Instrum (1)

G. S. Buller, R. D. Harkins, A. McCarthy, P. A. Hiskett, G. R. MacKinnon, G. R. Smith, R. Sung, A. M. Wallace, R. A. Lamb, K. D. Ridley, and J. G. Rarity, “Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting,” Rev. Sci. Instrum.  76, 083112 (2005).
[Crossref]

Science (1)

A. Kirmani, D. Venkatraman, D. Shin, A. Colaço, F. N. C. Wong, J. H. Shapiro, and V. K. Goyal, “First-photon imaging,” Science 343, 58–61 (2014).
[Crossref]

Other (9)

Y. Altmann, R. Tobin, A. Maccarone, X. Ren, A. Mccarthy, G. Buller, and S. Mclaughlin, “Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model,” in Proc. European Signal Processing Conf. (EUSIPCO) (Kos, Greece, 2017).

M. Pereyra, N. Whiteley, C. Andrieu, and J.-Y. Tourneret, “Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Gold Coast, Australia, 2014).

I. Jolliffe, Principal Component Analysis, Springer Series in Statistics (Springer, 2002).

J. J. Degnan and C. T. Field, “Moderate to high altitude, single photon sensitive, 3D imaging lidars,” Proc. SPIE 9114, Advanced Photon Counting Techniques VIII, 91140H (May 28, 2014).

P. Chhabra, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and A. Wallace, “Discriminating underwater lidar target signatures using sparse multi-spectral depth codes,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin, and G. S. Buller, “Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments,” IEEE Trans. Comput. Imaging, in press (2017).
[Crossref]

Y. Altmann, A. Maccarone, A. McCarthy, G. Buller, and S. McLaughlin, “Joint spectral clustering and range estimation for 3d scene reconstruction using multispectral lidar waveforms,” in Proc. European Signal Processing Conf. (EUSIPCO) (Budapest, Hungary, 2016).

Y. Altmann, A. Maccarone, A. McCarthy, G. S. Buller, and S. McLaughlin, “Joint range estimation and spectral classification for 3D scene reconstruction using multispectral lidar waveforms,” in “Proc. IEEE-SP Workshop Stat. and Signal Processing,” (Palma de Mallorca, Spain, 2016).

Y. Altmann, A. Maccarone, A. Halimi, A. McCarthy, G. S. Buller, and S. McLaughlin, “Efficient range estimation and material quantification from multispectral lidar waveforms,” in Sensor Signal Processing for Defence (SSPD) Conference (Edinburgh, U.K., 2016).

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Figures (13)

Fig. 1
Fig. 1 Experimental layout: pulsed laser illumination is provided by the supercontinuum laser source spectrally tunable via an acousto-optic tunable filter (AOTF). The transceiver raster scans the pulsed laser across the target and collects some of the scattered light from the target. The scattered return photons are coupled into an optical fiber and directed to the electrically gated single-photon detector. The laser provides an electrical synchronous start signal to the TCSPC module. The stop signal for the TCSPC module is provided by the SPAD. The picosecond resolution timing information was transferred to a desktop computer (not shown in diagram) from the TCSPC module.
Fig. 2
Fig. 2 Photographs of targets: (a) shows Target 1 which contains green clay objects and natural materials, fixed to a backplane; and (b) shows Target 2 which was composed of 14 clay materials with different colours. The recorded images consist of 200 × 200 pixels (the scanned target areas were approximately 50 × 50 mm) and the targets were placed at 1.85 meters from the system.
Fig. 3
Fig. 3 Example of distribution of photon counts for an average of 1 photon per pixel and per wavelength band. Target 2 was used for this example. Top row: Spatial distribution of photon counts recorded at wavelengths of 500 nm (left); 700 nm (middle); and integrated over the 33 spectral bands. Bottom row: Distribution of the photon counts at wavelengths of 500 nm (left); 700 nm (middle); and integrated over all the 33 spectral bands (right).
Fig. 4
Fig. 4 Analysis of Target 1 shown in Fig. 2 (a). The first column depicts the estimated depth profile (in mm), the reference range being arbitrarily set to the backplane range and the scale being the set to match those used in Fig. 11. The second depicts color classification maps. The third column depicts the spectral signatures of the most prominent classes, projected onto the first and second axes obtained using PCA. Each of these subplots illustrates the similarity between the estimated spectral signatures. Rows a) to d) depict results obtained with 33, 16, 8 and 4 wavelengths, regularly spaced within the 500–820 nm range (with an average of 10 detected photons per pixel, for each spectral band).
Fig. 5
Fig. 5 Spectral classification of Target 1 shown in Fig. 2(a) with an average of 10 and 1 detected photons per pixel, for each spectral band. The right-hand column corresponds to observing around 1/16 pixels from the original 190 × 190 pixel scan (and discarding all other pixels).
Fig. 6
Fig. 6 Examples of estimated spectral signatures for the classes associated with the materials #4 (red) and #9 (blue) of the Target 1, with 33 wavelengths. The vertical dashed lines represent the selected wavelengths when considering only 4 spectral bands. The solid lines represent the posterior mean while the coloured regions represent confidence intervals of ±3 standard deviations around the posterior mean.
Fig. 7
Fig. 7 Estimated spectral signatures of the classes associated with the materials #4 (red) and #9 (blue) of the Target 1 (190 × 190 pixel scan). This figure compares the estimated spectral signatures using 8, 16 and 33 wavelengths and with an average of 1 detected photon per pixel and per band. The vertical dashed lines represent the selected wavelengths when considering only 8 spectral bands. The solid lines represent the posterior mean while the coloured regions represent confidence intervals of ±3 standard deviations around the posterior mean.
Fig. 8
Fig. 8 Analysis of Target 2 shown in Fig. 2(b). The first column depicts the estimated depth profile (in mm), the reference range being arbitrarily set to the backplane range. The second depicts color classification maps. As in Fig. 4, the third column depicts the spectral signatures of the most prominent classes, projected onto the first and second axes obtained using PCA. Each of these subplots illustrates the similarity between the estimated spectral signatures. Rows a) and b) depict results obtained with an average of 1 detected photon per pixel, for each spectral band.
Fig. 9
Fig. 9 Example of spectral classification of Target 2 shown in Fig. 2 (b) with an average of 10 and 1 detected photons per pixel, for each spectral band.
Fig. 10
Fig. 10 Estimated spectral signatures of the most representative classes associated with the Target 2, with an average of 10 detected photons per pixel, for each spectral band and using 33 wavelengths. The line colours correspond to the colours used in the classification maps in Fig. 9 and the numbering of the legend corresponds to the numbering using in Fig. 2 (b).
Fig. 11
Fig. 11 Estimated range profile of Target 1 shown in Fig. 2(a) with an average of 10 and 1 detected photons per pixel, for each spectral band. As depicted in Table 1, ranging performance degrades when reducing the number of spectral bands from 33 to 4. Dark (resp. white) pixels correspond to pixels the closest (resp. the farthest) from the imaging system. Note the depth scale is in mm.
Fig. 12
Fig. 12 Estimated range profile of Target 2 shown in Fig. 2(b) with an average of 10 and 1 detected photons per pixel, for each spectral band.
Fig. 13
Fig. 13 Example of range marginal a posteriori pdfs as a function of the number of spectral bands considered, with an average of 10 (left) and 1 (right) detected photons per pixel, for each spectral band (190 × 190 pixels scan). The dashed lines represent the actual position of the target with respect to the backplane (which corresponds to dn = 0).

Tables (1)

Tables Icon

Table 1 Range root mean squared errors (RMSEs, in mm) for both targets, with different number of wavelengths, pixel format and photon number per pixel. The first (resp. second) numbers stand for the RMSEs obtained with (resp. without) the TV-based range regularization.

Equations (4)

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

y n , , t | λ n , , t n ~ P ( λ n , g n , ( t t n ) )
f ( Y | z , M , t ) = n f ( y n | z n = k , μ k , t n ) ,
f ( t , M , z , θ | Y , Φ ) f ( Y | t , M , z ) f ( t | c ) f ( M θ ) f ( z | c ) .
RMSE = n = 1 N ( d n d ^ n ) 2 ,

Metrics