Abstract

Multispectral imaging (MSI) is widely used in terrestrial applications to help increase the discriminability between objects of interest. While MSI has shown potential for underwater geological and biological surveys, it is thus far rarely applied underwater. This is primarily due to the fact light propagation in water is subject to wavelength dependent attenuation and tough working conditions in the deep ocean. In this paper, a novel underwater MSI system based on a tunable light source is presented which employs a monochrome still image camera with flashing, pressure neutral color LEDs. Laboratory experiments and field tests were performed. Results from the lab experiments show an improvement of 76.66% on discriminating colors on a checkerboard by using the proposed imaging system over the use of an RGB camera. The field tests provided in situ MSI observations of pelagic fauna, and showed the first evidence that the system is capable of acquiring useful imagery under real marine conditions.

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

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2018 (1)

T. A. Carrino, A. P. Crósta, C. L. B. Toledo, and A. M. Silva, “Hyperspectral remote sensing applied to mineral exploration in Southern Peru: A multiple data integration approach in the chapi chiara gold prospect,” Int. J. Appl. Earth Obs. 64, 287–300 (2018).
[Crossref]

2017 (7)

H. Pu, D. Liu, J.-H. Qu, and D.-W. Sun, “Applications of imaging spectrometry in inland water quality monitoring-a review of recent developments,” Water, Air, & Soil Pollution 228, 131 (2017).
[Crossref]

V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
[Crossref]

A. I. Ropodi, E. Z. Panagou, and G.-J. E. Nychas, “Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat,” Food Control 73, 57–63 (2017).
[Crossref]

A. A. Mogstad and G. Johnsen, “Spectral characteristics of coralline algae: a multi-instrumental approach, with emphasis on underwater hyperspectral imaging,” Appl. Opt. 56, 9957–9975 (2017).
[Crossref]

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
[Crossref]

A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
[Crossref]

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

2016 (4)

Y. Guo, H. Song, H. Liu, H. Wei, P. Yang, S. Zhan, H. Wang, H. Huang, N. Liao, Q. Mu, J. Leng, and W. Yang, “Model-based restoration of underwater spectral images captured with narrowband filters,” Opt. Express 24, 13101–13120 (2016).
[Crossref] [PubMed]

X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
[Crossref]

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
[Crossref]

G. Johnsen, M. Ludvigsen, A. Sørensen, and L. M. S. Aas, “The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications,” IFAC-PapersOnLine 49, 476–481 (2016).
[Crossref]

2015 (1)

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
[Crossref]

2014 (2)

D. G. Zawada and C. H. Mazel, “Fluorescence-based classification of caribbean coral reef organisms and substrates,” PloS one 9, e84570 (2014).
[Crossref] [PubMed]

R. Pettersen, G. Johnsen, P. Bruheim, and T. Andreassen, “Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms,” Org. Divers. Evol. 14, 237–246 (2014).
[Crossref]

2012 (1)

I. Leiper, S. Phinn, and A. G. Dekker, “Spectral reflectance of coral reef Benthos and substrate assemblages on Heron Reef, Australia,” Int. J. Remote Sens. 33, 3946–3965 (2012).
[Crossref]

2011 (1)

I. Vasilescu, C. Detweiler, and D. Rus, “Color-accurate underwater imaging using perceptual adaptive illumination,” Auton. Robot. 31, 285 (2011).
[Crossref]

2009 (1)

2004 (1)

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
[Crossref]

2003 (2)

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal 14, 79–116 (2003).

M. K. Griffin and H.-h. K. Burke, “Compensation of hyperspectral data for atmospheric effects,” Lincoln Laboratory Journal 14, 29–54 (2003).

1994 (1)

1962 (1)

D. Swinehart, “The beer-lambert law,” J. Chem. Educ 39, 333 (1962).
[Crossref]

Aas, L. M.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Aas, L. M. S.

G. Johnsen, M. Ludvigsen, A. Sørensen, and L. M. S. Aas, “The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications,” IFAC-PapersOnLine 49, 476–481 (2016).
[Crossref]

Ø. Sture, M. Ludvigsen, and L. M. S. Aas, “Autonomous underwater vehicles as a platform for underwater hyperspectral imaging,” in Proceedings of IEEE/MTS OCEANS’17 (IEEE, 2017), pp. 1–8.

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Akkaynak, D.

D. Akkaynak, E. Chan, J. J. Allen, and R. T. Hanlon, “Using spectrometry and photography to study color underwater,” in Proceedings of IEEE/MTS OCEANS’11 (IEEE, 2011), pp. 1–8.

Allen, J. J.

D. Akkaynak, E. Chan, J. J. Allen, and R. T. Hanlon, “Using spectrometry and photography to study color underwater,” in Proceedings of IEEE/MTS OCEANS’11 (IEEE, 2011), pp. 1–8.

Andreassen, T.

R. Pettersen, G. Johnsen, P. Bruheim, and T. Andreassen, “Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms,” Org. Divers. Evol. 14, 237–246 (2014).
[Crossref]

Ares, M.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Beijbom, O.

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
[Crossref]

Beuchel, F.

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Bian, L.

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
[Crossref]

Bishop, C. M.

C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).

Blasinski, H.

H. Blasinski and J. Farrell, “Computational multispectral flash,” in Proceedings of IEEE Conference on Computational Photography (IEEE, 2017), pp. 1–10.

Bongiorno, D. L.

D. L. Bongiorno, M. Bryson, T. C. Bridge, D. G. Dansereau, and S. B. Williams, “Coregistered hyperspectral and stereo image seafloor mapping from an autonomous underwater vehicle,” J. Field Robot. (2017).
[Crossref]

Bosch, T.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Bouchard, M. B.

Brady, D. J.

X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
[Crossref]

Bridge, T. C.

D. L. Bongiorno, M. Bryson, T. C. Bridge, D. G. Dansereau, and S. B. Williams, “Coregistered hyperspectral and stereo image seafloor mapping from an autonomous underwater vehicle,” J. Field Robot. (2017).
[Crossref]

Bruheim, P.

R. Pettersen, G. Johnsen, P. Bruheim, and T. Andreassen, “Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms,” Org. Divers. Evol. 14, 237–246 (2014).
[Crossref]

Bryson, M.

D. L. Bongiorno, M. Bryson, T. C. Bridge, D. G. Dansereau, and S. B. Williams, “Coregistered hyperspectral and stereo image seafloor mapping from an autonomous underwater vehicle,” J. Field Robot. (2017).
[Crossref]

Burgess, S. A.

Burke, H.-h. K.

M. K. Griffin and H.-h. K. Burke, “Compensation of hyperspectral data for atmospheric effects,” Lincoln Laboratory Journal 14, 29–54 (2003).

Bytingsvik, J.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Camus, L.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Cao, X.

X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
[Crossref]

Carin, L.

X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
[Crossref]

Carrino, T. A.

T. A. Carrino, A. P. Crósta, C. L. B. Toledo, and A. M. Silva, “Hyperspectral remote sensing applied to mineral exploration in Southern Peru: A multiple data integration approach in the chapi chiara gold prospect,” Int. J. Appl. Earth Obs. 64, 287–300 (2018).
[Crossref]

Chan, E.

D. Akkaynak, E. Chan, J. J. Allen, and R. T. Hanlon, “Using spectrometry and photography to study color underwater,” in Proceedings of IEEE/MTS OCEANS’11 (IEEE, 2011), pp. 1–8.

Chang, C.-C.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
[Crossref]

Chang, C.-I.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
[Crossref]

Chen, B. R.

Chen, F.

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
[Crossref]

Chennu, A.

A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
[Crossref]

Crósta, A. P.

T. A. Carrino, A. P. Crósta, C. L. B. Toledo, and A. M. Silva, “Hyperspectral remote sensing applied to mineral exploration in Southern Peru: A multiple data integration approach in the chapi chiara gold prospect,” Int. J. Appl. Earth Obs. 64, 287–300 (2018).
[Crossref]

D’Amico, F. M.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
[Crossref]

Dai, Q.

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
[Crossref]

X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
[Crossref]

Dansereau, D. G.

D. L. Bongiorno, M. Bryson, T. C. Bridge, D. G. Dansereau, and S. B. Williams, “Coregistered hyperspectral and stereo image seafloor mapping from an autonomous underwater vehicle,” J. Field Robot. (2017).
[Crossref]

de Beer, D.

A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
[Crossref]

De’ath, G.

A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
[Crossref]

Dekker, A. G.

I. Leiper, S. Phinn, and A. G. Dekker, “Spectral reflectance of coral reef Benthos and substrate assemblages on Heron Reef, Australia,” Int. J. Remote Sens. 33, 3946–3965 (2012).
[Crossref]

Delpueyo, X.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Destain, M.-F.

V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
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Detweiler, C.

I. Vasilescu, C. Detweiler, and D. Rus, “Color-accurate underwater imaging using perceptual adaptive illumination,” Auton. Robot. 31, 285 (2011).
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Dierssen, H.

G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
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Du, Y.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
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Dumont, B.

V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
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Eide, I.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Ekehaug, S.

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Fabricius, K. E.

A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
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A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
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H. Blasinski and J. Farrell, “Computational multispectral flash,” in Proceedings of IEEE Conference on Computational Photography (IEEE, 2017), pp. 1–10.

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G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
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Gleason, A.

A. Gleason, R. Reid, and K. Voss, “Automated classification of underwater multispectral imagery for coral reef monitoring,” in Proceedings of IEEE/MTS OCEANS’07 (IEEE, 2007), pp. 1–8.

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M. K. Griffin and H.-h. K. Burke, “Compensation of hyperspectral data for atmospheric effects,” Lincoln Laboratory Journal 14, 29–54 (2003).

Grossberg, M. D.

J.-I. Park, M.-H. Lee, M. D. Grossberg, and S. K. Nayar, “Multispectral imaging using multiplexed illumination,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Guo, Y.

Guyomarch, J.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Hanlon, R. T.

D. Akkaynak, E. Chan, J. J. Allen, and R. T. Hanlon, “Using spectrometry and photography to study color underwater,” in Proceedings of IEEE/MTS OCEANS’11 (IEEE, 2011), pp. 1–8.

Hansen, I. M.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Hillman, E. M.

Holden, H.

H. Holden and E. LeDrew, “Hyperspectral discrimination of healthy versus stressed corals using in situ reflectance,” J. Coastal Res.850–858 (2001).

Hoyt, C. C.

Huang, H.

Huang, K.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
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Hui, W.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
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Jensen, J. O.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
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Jin, S.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
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A. A. Mogstad and G. Johnsen, “Spectral characteristics of coralline algae: a multi-instrumental approach, with emphasis on underwater hyperspectral imaging,” Appl. Opt. 56, 9957–9975 (2017).
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G. Johnsen, M. Ludvigsen, A. Sørensen, and L. M. S. Aas, “The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications,” IFAC-PapersOnLine 49, 476–481 (2016).
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R. Pettersen, G. Johnsen, P. Bruheim, and T. Andreassen, “Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms,” Org. Divers. Evol. 14, 237–246 (2014).
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G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
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G. Johnsen, Z. Volent, E. Sakshaug, F. Sigernes, and L. H. Pettersson, Remote sensing in the Barents Sea (Tapir Academic, 2009), Chap. 6.

Kline, D. I.

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
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Kriegman, D.

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
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Kwasnitschka, T.

J. Sticklus and T. Kwasnitschka, “Verfahren und vorrichtung zur herstellung von in vergussmasse vergossenen leuchten,” (2015). DE Patent 102,014,118,672.

le Floch, S.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

LeDrew, E.

H. Holden and E. LeDrew, “Hyperspectral discrimination of healthy versus stressed corals using in situ reflectance,” J. Coastal Res.850–858 (2001).

Lee, M.-H.

J.-I. Park, M.-H. Lee, M. D. Grossberg, and S. K. Nayar, “Multispectral imaging using multiplexed illumination,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Leemans, V.

V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
[Crossref]

Leiper, I.

I. Leiper, S. Phinn, and A. G. Dekker, “Spectral reflectance of coral reef Benthos and substrate assemblages on Heron Reef, Australia,” Int. J. Remote Sens. 33, 3946–3965 (2012).
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Leng, J.

Letnes, P. A.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Li, Z.

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
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Lin, S.

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X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
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Liu, D.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
[Crossref]

H. Pu, D. Liu, J.-H. Qu, and D.-W. Sun, “Applications of imaging spectrometry in inland water quality monitoring-a review of recent developments,” Water, Air, & Soil Pollution 228, 131 (2017).
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Liu, H.

Ludvigsen, M.

G. Johnsen, M. Ludvigsen, A. Sørensen, and L. M. S. Aas, “The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications,” IFAC-PapersOnLine 49, 476–481 (2016).
[Crossref]

Ø. Sture, M. Ludvigsen, and L. M. S. Aas, “Autonomous underwater vehicles as a platform for underwater hyperspectral imaging,” in Proceedings of IEEE/MTS OCEANS’17 (IEEE, 2017), pp. 1–8.

G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
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X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
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D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal 14, 79–116 (2003).

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D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal 14, 79–116 (2003).

Marlier, G.

V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
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V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
[Crossref]

Mitchell, B. G.

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
[Crossref]

Mogstad, A. A.

Moline, M.

G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
[Crossref]

Morris, H. R.

Mu, Q.

Nayar, S. K.

J.-I. Park, M.-H. Lee, M. D. Grossberg, and S. K. Nayar, “Multispectral imaging using multiplexed illumination,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Neal, B. P.

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
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X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
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Nychas, G.-J. E.

A. I. Ropodi, E. Z. Panagou, and G.-J. E. Nychas, “Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat,” Food Control 73, 57–63 (2017).
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Panagou, E. Z.

A. I. Ropodi, E. Z. Panagou, and G.-J. E. Nychas, “Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat,” Food Control 73, 57–63 (2017).
[Crossref]

Park, J.-I.

J.-I. Park, M.-H. Lee, M. D. Grossberg, and S. K. Nayar, “Multispectral imaging using multiplexed illumination,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

Pellacani, G.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
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Pettersen, R.

R. Pettersen, G. Johnsen, P. Bruheim, and T. Andreassen, “Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms,” Org. Divers. Evol. 14, 237–246 (2014).
[Crossref]

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
[Crossref]

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Pettersson, L. H.

G. Johnsen, Z. Volent, E. Sakshaug, F. Sigernes, and L. H. Pettersson, Remote sensing in the Barents Sea (Tapir Academic, 2009), Chap. 6.

Phinn, S.

I. Leiper, S. Phinn, and A. G. Dekker, “Spectral reflectance of coral reef Benthos and substrate assemblages on Heron Reef, Australia,” Int. J. Remote Sens. 33, 3946–3965 (2012).
[Crossref]

Pu, H.

H. Pu, D. Liu, J.-H. Qu, and D.-W. Sun, “Applications of imaging spectrometry in inland water quality monitoring-a review of recent developments,” Water, Air, & Soil Pollution 228, 131 (2017).
[Crossref]

Puig, S.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Qu, J.-H.

H. Pu, D. Liu, J.-H. Qu, and D.-W. Sun, “Applications of imaging spectrometry in inland water quality monitoring-a review of recent developments,” Water, Air, & Soil Pollution 228, 131 (2017).
[Crossref]

Receveur, J.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Reid, R.

A. Gleason, R. Reid, and K. Voss, “Automated classification of underwater multispectral imagery for coral reef monitoring,” in Proceedings of IEEE/MTS OCEANS’07 (IEEE, 2007), pp. 1–8.

Ren, H.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43, 1777–1786 (2004).
[Crossref]

Rey-Barroso, L.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Roberts, P. L.

T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
[Crossref]

Ropodi, A. I.

A. I. Ropodi, E. Z. Panagou, and G.-J. E. Nychas, “Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat,” Food Control 73, 57–63 (2017).
[Crossref]

Royo, S.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Rus, D.

I. Vasilescu, C. Detweiler, and D. Rus, “Color-accurate underwater imaging using perceptual adaptive illumination,” Auton. Robot. 31, 285 (2011).
[Crossref]

Sakshaug, E.

G. Johnsen, Z. Volent, E. Sakshaug, F. Sigernes, and L. H. Pettersson, Remote sensing in the Barents Sea (Tapir Academic, 2009), Chap. 6.

Sanabria, F.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
[Crossref]

Shaw, G. A.

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal 14, 79–116 (2003).

Shi, Q.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
[Crossref]

Sigernes, F.

G. Johnsen, Z. Volent, E. Sakshaug, F. Sigernes, and L. H. Pettersson, Remote sensing in the Barents Sea (Tapir Academic, 2009), Chap. 6.

Silva, A. M.

T. A. Carrino, A. P. Crósta, C. L. B. Toledo, and A. M. Silva, “Hyperspectral remote sensing applied to mineral exploration in Southern Peru: A multiple data integration approach in the chapi chiara gold prospect,” Int. J. Appl. Earth Obs. 64, 287–300 (2018).
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Situ, G.

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
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Solomita, G.

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
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Song, H.

Søreide, F.

G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
[Crossref]

Sørensen, A.

G. Johnsen, M. Ludvigsen, A. Sørensen, and L. M. S. Aas, “The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications,” IFAC-PapersOnLine 49, 476–481 (2016).
[Crossref]

Steen, K. J.

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Sticklus, J.

J. Sticklus and T. Kwasnitschka, “Verfahren und vorrichtung zur herstellung von in vergussmasse vergossenen leuchten,” (2015). DE Patent 102,014,118,672.

Sture, Ø.

Ø. Sture, M. Ludvigsen, and L. M. S. Aas, “Autonomous underwater vehicles as a platform for underwater hyperspectral imaging,” in Proceedings of IEEE/MTS OCEANS’17 (IEEE, 2017), pp. 1–8.

Sun, D.-W.

H. Pu, D. Liu, J.-H. Qu, and D.-W. Sun, “Applications of imaging spectrometry in inland water quality monitoring-a review of recent developments,” Water, Air, & Soil Pollution 228, 131 (2017).
[Crossref]

Suo, J.

L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. -UK 6, 24752 (2016).
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Tassara, L.

P. A. Letnes, I. M. Hansen, L. M. Aas, I. Eide, R. Pettersen, L. Tassara, J. Receveur, S. le Floch, J. Guyomarch, L. Camus, and J. Bytingsvik, “Underwater hyperspectral classification of deep sea corals exposed to a toxic compound,” bioRxiv (2017).

Tegdan, J.

J. Tegdan, S. Ekehaug, I. M. Hansen, L. M. S. Aas, K. J. Steen, R. Pettersen, F. Beuchel, and L. Camus, “Underwater hyperspectral imaging for environmental mapping and monitoring of seabed habitats,” in Proceedings of IEEE/MTS OCEANS’15 (IEEE, 2015), pp. 1–6.

Tian, J.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
[Crossref]

Toledo, C. L. B.

T. A. Carrino, A. P. Crósta, C. L. B. Toledo, and A. M. Silva, “Hyperspectral remote sensing applied to mineral exploration in Southern Peru: A multiple data integration approach in the chapi chiara gold prospect,” Int. J. Appl. Earth Obs. 64, 287–300 (2018).
[Crossref]

Treado, P. J.

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T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
[Crossref]

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[Crossref]

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G. Johnsen, Z. Volent, H. Dierssen, R. Pettersen, M. Van Ardelan, F. Søreide, P. Fearns, M. Ludvigsen, and M. Moline, “Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties,” in Subsea Optics and Imaging, J. Watson and O. Zielinski, eds. (Woodhead, 2013).
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S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
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[Crossref]

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S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel fourier transform technique,” Sci. Rep. -UK 7, 45209 (2017).
[Crossref]

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Appl. Spectrosc. (1)

Auton. Robot. (1)

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IEEE Signal Proc. Mag. (1)

X. Cao, T. Yue, X. Lin, S. Lin, X. Yuan, Q. Dai, L. Carin, and D. J. Brady, “Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world,” IEEE Signal Proc. Mag. 33, 95–108 (2016).
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G. Johnsen, M. Ludvigsen, A. Sørensen, and L. M. S. Aas, “The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications,” IFAC-PapersOnLine 49, 476–481 (2016).
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Int. J. Appl. Earth Obs. (1)

T. A. Carrino, A. P. Crósta, C. L. B. Toledo, and A. M. Silva, “Hyperspectral remote sensing applied to mineral exploration in Southern Peru: A multiple data integration approach in the chapi chiara gold prospect,” Int. J. Appl. Earth Obs. 64, 287–300 (2018).
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J. Biomed. Opt. (1)

X. Delpueyo, M. Vilaseca, S. Royo, M. Ares, L. Rey-Barroso, F. Sanabria, S. Puig, J. Malvehy, G. Pellacani, F. Noguero, G. Solomita, and T. Bosch, “Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study,” J. Biomed. Opt. 22, 065006 (2017).
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D. Swinehart, “The beer-lambert law,” J. Chem. Educ 39, 333 (1962).
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D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal 14, 79–116 (2003).

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Opt. Eng. (1)

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Org. Divers. Evol. (1)

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PloS one (1)

D. G. Zawada and C. H. Mazel, “Fluorescence-based classification of caribbean coral reef organisms and substrates,” PloS one 9, e84570 (2014).
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Proc. SPIE (1)

V. Leemans, G. Marlier, M.-F. Destain, B. Dumont, and B. Mercatoris, “Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging,” Proc. SPIE 10213, 102130I (2017).
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T. Treibitz, B. P. Neal, D. I. Kline, O. Beijbom, P. L. Roberts, B. G. Mitchell, and D. Kriegman, “Wide field-of-view fluorescence imaging of coral reefs,” Sci. Rep. -UK 5, 7694 (2015).
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A. Chennu, P. Färber, G. De’ath, D. de Beer, and K. E. Fabricius, “A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats,” Sci. Rep. -UK 7, 7122 (2017).
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Figures (12)

Fig. 1
Fig. 1 Demonstration of the spectral dissimilarity criterion. C11 through C13 are different samples on object C1, C21 through C23 are different samples on object C2. The spectral signature of each sample is the averaged signatures of all pixels forming the sample. The 9-by-9 matrix on the right can be divided into sub-matrices, where sub-matrix C1 and sub-matrix C2 are within-class similarity measures of object C1 and C2, and the elements in sub-matrix M are between-class similarity measure of samples in object C1 and C2. Elements on the main diagonal represent similarity measures of samples with themselves. The dissimilarity of the two objects C1 and C2 depends on both within-class and between-class measures.
Fig. 2
Fig. 2 Setup of TuLUMIS. (a) The scheme of the system comprising LEDs, a camera, and components for power supply, synchronization, control, data storage, and power/signal transmission. The Arduino micro board is programmed to synchronize the flash of LEDs and the acquisition time of the camera. (b) The components of TuLUMIS, with close-ups of the internal structure of the water tight housing for the camera and control circuits, and a cast LED.
Fig. 3
Fig. 3 The relative radiances of the eight LEDs at 700 mA are measured by a spectrometer, and the spectra are plotted as colored curves in (a), while the nominal central wavelengths of the LEDs tested by the manufacturer at 500 mA and 25°C are listed in the legend. The thin black curve in (a) is the spectral response of the camera. The height of each square in (b) is the integration of each LED’s spectral radiance and the camera’s response, and the width of each square shows the full width at half maximum (FWHM) of the corresponding LED’s spectral radiance.
Fig. 4
Fig. 4 Setup of the lab experiment.
Fig. 5
Fig. 5 Preprocessing of the raw image of the checker board taken by the RGB camera. (a) shows the original image with white and black frames marking selected color and white areas respectively. (b) shows the non-uniform illuminance background calculated by third order polynomial fitting of the selected white area. Contours are augmented to highlight the intensity change of the illuminance from bright (the center) to dim (the edge). (c) shows the image corrected by dividing (a) by (b) in all channels. The indices are assigned according to the hue of the colors. (d) shows the selected color samples with black and white frames marking selected area. (e) shows the colors extracted from the corrected image by taking the average of all pixels in the marked area of each color block. All colors are transformed according to SonyA7SM2-Generic’s ICC profile for visualization.
Fig. 6
Fig. 6 Relative spectral reflectance of the 33 color panels on the checkerboard measured by the RGB method (dashed lines), the MS method (solid lines), and the SP method (dotted lines). The wavelength range of each sub-figure covers the visible spectrum from 400 nm to 700 nm, and relative reflectances range from 0 to 1, with axis ticks shown in the legend in the lower left corner. Order of the sub-figures and color of the curves are consistent with Fig. 5(e). Compared to the RGB method, the MS method with eight channels acquires a finer-resolved spectral information. The spectrl resolution of MS is lower than that of the SP method but more than four million spectral measurements can be conducted in parallel (for all the pixels vs. for one point measurement).
Fig. 7
Fig. 7 Results of similarity measures SAM (the first row) and SID (the second row) of the RGB method (the first column), the MS method (the second column) and the SP method (the third column). Each matrix is a 330-by-330 matrix (10 samples per color) and each element represents the similarity measure between the corresponding two samples. The color bars are in logarithm scale. Detailed comparisons are shown in Fig. 8 and Fig. 9.
Fig. 8
Fig. 8 Difference matrices of the spectral dissimilarity (or normalized between-class SAM) Δ(SAM) (a) between the MS method and the RGB method, and (c) between the SP method and the RGB method. The histograms (b) and (d) show the number of elements counted in the corresponding difference matrix (a) and (c), respectively. 76.66% of the between-class elements are increased by using the MS method over the use of the RGB method, and 99.99% of the between-class elements are increased by using the SP method over the use of the RGB method. The bars in the histograms are stacked by the colors of the related samples on the checkerboard.
Fig. 9
Fig. 9 Difference matrices of the spectral dissimilarity (or normalized between-class SID) Δ(SID) (a) between the MS method and the RGB method, and (c) between the SP method and the RGB method. The histograms (b) and (d) show the number of elements counted in the corresponding difference matrix (a) and (c), respectively. 68.24% of the between-class elements are increased by using the MS method over the use of the RGB method, and 99.76% of the between-class elements are increased by using the SP method over the use of the RGB method. The bars in the histograms are stacked by the colors of the related samples on the checkerboard.
Fig. 10
Fig. 10 Setups of TuLUMIS during cruise MSM 61. (a) The PELAGIOS frame on which TuLUMIS was carried. (b) A schematic illustration of the LED array layout and camera focal length setting. A white board was used as a reference to balance the radiance of each LED and to calibrate the effect of wavelength-dependent light attenuation.
Fig. 11
Fig. 11 A sergestid shrimp observed during the cruise MSM61 by the TuLUMIS. The monochrome images in eight spectral channels are shown on the left, with a fused pseudo-color image on the right.
Fig. 12
Fig. 12 Difference matrices of (a) normalized between-class SAM (i.e. dissimilarity Δ(SAM)), and (c) normalized between-class SID (i.e. dissimilarity Δ(SID)) between the MS method and the RGB method after dimensionality reduction using LDA. The histograms (b) and (d) show the number of elements counted in the corresponding difference matrix (a) and (c), respectively. In the case of using LDA, 95.82% of the between-class elements are increased by using the MS method with SAM over the use of the RGB method, and 78.06% of the between-class elements are increased by using the MS method with SID over the use of the RGB method. The bars in the histograms are stacked by the colors of the related samples on the checkerboard.

Equations (11)

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I = λ c I s ( λ ) e α ( λ ) d 1 R ( λ ) e α ( λ ) d 2 C ( λ ) d λ ,
SAM ( s , r ) = θ = arccos s r s r s , r n ,
p i = | s i | j = 1 n | s j | ,
SID ( s , r ) = D ( s r ) + D ( r s )
D ( s r ) = i = 1 m p i log ( p i / q i )
D ( r s ) = i = 1 m q i log ( q i / p i )
C 1 i j ( Γ ) = Γ ( c 1 i , c 1 j ) , i , j = 1 , 2 , , m 1
C 2 i j ( Γ ) = Γ ( c 2 i , c 2 j ) , i , j = 1 , 2 , , m 2
M i j ( Γ ) = Γ ( c 1 i , c 2 j ) i = 1 , 2 , , m 1 ; j = 1 , 2 , , m 2
μ ( Γ ) ( C 1 , C 2 ) = i = 1 m 1 j = 1 , j i m 1 C 1 i j + i = 1 m 1 j = 1 , j i m 1 C 2 i j m 1 ( m 1 1 ) + m 2 ( m 2 1 )
Δ i j ( Γ ) = M i j μ ( Γ ) ( C 1 , C 2 )

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