TuLUMIS-a tunable LED-based underwater multispectral imaging system

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 that 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 OCIS codes: (010.4450) Oceanic optics; (110.4234) Multispectral and hyperspectral imaging; (330.6180) Spectral discrimination; (120.4820) Optical systems. References and links 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). 2. 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). 3. 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). 4. 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). 5. G. Johnsen, Z. Volent, E. Sakshaug, F. Sigernes, and L. H. Pettersson, Remote sensing in the Barents Sea (Tapir Academic, 2009), Chap. 6. 6. 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, O. Zielinski, eds. (Woodhead, 2013). 7. 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. 8. 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). 9. 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). 10. 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. 11. 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). 12. 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). Vol. 26, No. 6 | 19 Mar 2018 | OPTICS EXPRESS 7811 #320160 https://doi.org/10.1364/OE.26.007811 Journal © 2018 Received 19 Jan 2018; revised 12 Feb 2018; accepted 12 Feb 2018; published 16 Mar 2018 13. 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). 14. 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). 15. M. K. Griffin and H.-h. K. Burke, “Compensation of hyperspectral data for atmospheric effects,” Lincoln Laboratory Journal 14, 29–54 (2003). 16. C. Mobley, E. Boss, and C. Roesler, Ocean Optics Web Book http://www.oceanopticsbook.info/. 17. I. Vasilescu, C. Detweiler, and D. Rus, “Color-accurate underwater imaging using perceptual adaptive illumination,” Auton. Robot. 31, 285 (2011). 18. 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). 19. 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). 20. D. G. Zawada and C. H. Mazel, “Fluorescence-based classification of caribbean coral reef organisms and substrates,” PloS one 9, e84570 (2014). 21. H. Holden and E. LeDrew, “Hyperspectral discrimination of healthy versus stressed corals using in situ reflectance,” J. Coastal Res. 850–858 (2001). 22. 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). 23. 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). 24. 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). 25. 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). 26. H. R. Morris, C. C. Hoyt, and P. J. Treado, “Imaging spectrometers for fluorescence and raman microscopy: acousto-optic and liquid crystal tunable filters,” Appl. Spectrosc. 48, 857–866 (1994). 27. 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. 28. 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. 29. H. Blasinski and J. Farrell, “Computational multispectral flash,” in Proceedings of IEEE Conference on Computational Photography (IEEE, 2017), pp. 1–10. 30. M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17, 15670–15678 (2009). 31. 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). 32. D. Swinehart, “The beer-lambert law,” J. Chem. Educ 39, 333 (1962). 33. 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). 34. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006). 35. D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal 14, 79–116 (2003). 36. Itseez, “Open source computer vision library,” https://github.com/opencv/opencv (2017). 37. J. Sticklus and T. Kwasnitschka, “Verfahren und vorrichtung zur herstellung von in vergussmasse vergossenen leuchten,” (2015). DE Patent 102,014,118,672. 38. Lumileds Holding B.V., “DS105 LUXEON Z color line product datasheet,” https://www.lumileds.com/ uploads/415/DS105-pdf (2017). 39. 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. 40. D. Coffin, “DCRaw Version 9.27,” https://www.cybercom.net/~dcoffin/dcraw/ (2016). 41. B. Fiedler, “Short cruise report RV Maria S. Merian MSM61,” https://www.ldf.uni-hamburg.de/ merian/wochenberichte/wochenberichte-merian/msm58-2-msm61/msm61-scr.pdf (2017).

[1]  Qing Ye,et al.  Hyperspectral imaging using the single-pixel Fourier transform technique , 2017, Scientific Reports.

[2]  Arjun Chennu,et al.  A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats , 2017, Scientific Reports.

[3]  E. Hillman,et al.  Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics. , 2009, Optics express.

[4]  Ellsworth F. LeDrew,et al.  Hyperspectral Discrimination of Healthy Versus Stressed Corals Using In Situ Reflectance , 2001 .

[5]  George-John E. Nychas,et al.  Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat , 2017 .

[6]  Carrick Detweiler,et al.  Color-accurate underwater imaging using perceptual adaptive illumination , 2010, Auton. Robots.

[7]  Benoît Mercatoris,et al.  Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging , 2017, Commercial + Scientific Sensing and Imaging.

[8]  Aksel Alstad Mogstad,et al.  Spectral characteristics of coralline algae: a multi-instrumental approach, with emphasis on underwater hyperspectral imaging , 2017 .

[9]  Martin Ludvigsen,et al.  Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties , 2013 .

[10]  Dean Calloway,et al.  Beer-Lambert Law , 1997 .

[11]  Adalene Moreira Silva,et al.  Hyperspectral remote sensing applied to mineral exploration in southern Peru: A multiple data integration approach in the Chapi Chiara gold prospect , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Geir Johnsen,et al.  Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms , 2013, Organisms Diversity & Evolution.

[13]  Oscar Beijbom,et al.  Wide Field-of-View Fluorescence Imaging of Coral Reefs , 2015, Scientific Reports.

[14]  Shree K. Nayar,et al.  Multispectral Imaging Using Multiplexed Illumination , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  A.C.R. Gleason,et al.  Automated classification of underwater multispectral imagery for coral reef monitoring , 2007, OCEANS 2007.

[16]  J. Bytingsvik,et al.  Underwater hyperspectral classification of deep sea corals exposed to a toxic compound , 2017, bioRxiv.

[17]  Asgeir J. Sørensen,et al.  The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications , 2016 .

[18]  Ningfang Liao,et al.  Model-based restoration of underwater spectral images captured with narrowband filters. , 2016, Optics express.

[19]  Gary A. Shaw,et al.  Hyperspectral Image Processing for Automatic Target Detection Applications , 2003 .

[20]  Stuart R. Phinn,et al.  Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef, Australia , 2012 .

[21]  Hongbin Pu,et al.  Applications of Imaging Spectrometry in Inland Water Quality Monitoring—a Review of Recent Developments , 2017, Water, Air, & Soil Pollution.

[22]  Patrick J. Treado,et al.  Imaging Spectrometers for Fluorescence and Raman Microscopy: Acousto-Optic and Liquid Crystal Tunable Filters , 1994 .

[23]  Charles H. Mazel,et al.  Fluorescence-Based Classification of Caribbean Coral Reef Organisms and Substrates , 2014, PloS one.

[24]  M. Griffin,et al.  Compensation of Hyperspectral Data for Atmospheric Effects , 2003 .

[25]  Martin Ludvigsen,et al.  Autonomous underwater vehicles as a platform for underwater hyperspectral imaging , 2017, OCEANS 2017 - Aberdeen.

[26]  Stephen Lin,et al.  Computational Snapshot Multispectral Cameras: Toward dynamic capture of the spectral world , 2016, IEEE Signal Processing Magazine.