Sensing and Imaging

, 20:35 | Cite as

Hyperspectral Imaging System: Development Aspects and Recent Trends

  • Vaibhav LodhiEmail author
  • Debashish Chakravarty
  • Pabitra Mitra
Overview / Summary paper


The convergence of spectroscopy and imaging technologies, emerge into a single sensing technology i.e., provides spatial and spectral information of the objects under investigation. The hyperspectral technique is one of the popular techniques used in numerous fields of study to determine size, shape, texture, material composition, morphology and external defects. The main advantage of this sensing technology lies in the fact that it is capable not only makes direct assessment of the material under study but also it can indicate the spatial distribution of the selected parameters. The aim of this paper is to present a detailed outline of the principles, background, acquisition methods, component descriptions and recent advances of the hyperspectral imaging systems for laboratory and industrial environments.


Hyperspectral imaging Spectroscopy Instruments Fundamentals Advancements 



  1. 1.
    Adam, E., Mutanga, O., & Rugege, D. (2010). Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetlands Ecology and Management, 18(3), 281–296. Scholar
  2. 2.
    Andrew, M. E., & Ustin, S. L. (2008). The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sensing of Environment, 112(12), 4301–4317. Scholar
  3. 3.
    Ayerden, N., Ghaderi, M., Silva, M., Emadi, A., Enoksson, P., Correia, J., De Graaf, G., & Wolffenbuttel, R. (2014). Design, fabrication and characterization of LVOF-based IR microspectrometers. In SPIE photonics Europe (p. 91300T). International Society for Optics and Photonics.Google Scholar
  4. 4.
    Bannari, A., Pacheco, A., Staenz, K., McNairn, H., & Omari, K. (2006). Estimating and mapping crop residues cover on agricultural lands using hyperspectral and ikonos data. Remote Sensing of Environment, 104(4), 447–459. Scholar
  5. 5.
    Bigas, M., Cabruja, E., Forest, J., & Salvi, J. (2006). Review of CMOS image sensors. Microelectronics Journal, 37(5), 433–451. Scholar
  6. 6.
    Brando, V. E., & Dekker, A. G. (2003). Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1378–1387. Scholar
  7. 7.
    Brown, A. J., Sutter, B., & Dunagan, S. (2008). The marte vnir imaging spectrometer experiment: Design and analysis. Astrobiology, 8(5), 1001–1011.CrossRefGoogle Scholar
  8. 8.
    Burr, T., & Hengartner, N. (2006). Overview of physical models and statistical approaches for weak gaseous plume detection using passive infrared hyperspectral imagery. Sensors, 6(12), 1721–1750. Scholar
  9. 9.
    Chen, J., Venkataraman, K., Bakin, D., Rodricks, B., Gravelle, R., Rao, P., et al. (2009). Digital camera imaging system simulation. IEEE Transactions on Electron Devices, 56(11), 2496–2505. Scholar
  10. 10.
    Chen, Y. R., Chao, K., & Kim, M. S. (2002). Machine vision technology for agricultural applications. Computers and Electronics in Agriculture, 36(2), 173–191. Scholar
  11. 11.
    Dai, Q., Ma, C., Suo, J., & Cao, X. (2014). Computational hyperspectral imaging. In JSAP-OSA joint symposia (p. 20p\_C4\_5). Optical Society of America.Google Scholar
  12. 12.
    Egan, C., Jacques, S., Wilson, M., Veale, M., Seller, P., Beale, A., et al. (2015). 3D chemical imaging in the laboratory by hyperspectral X-ray computed tomography. Scientific Reports, 5, 15979.CrossRefGoogle Scholar
  13. 13.
    Eichenholz, J.M. (2010). Sequential filter wheel multispectral imaging systems. In Applied industrial optics: Spectroscopy, imaging and metrology (p. ATuB2). Optical Society of America.
  14. 14.
    Eismann, M. T. (2012). Hyperspectral remote sensing. Bellingham: SPIE.CrossRefGoogle Scholar
  15. 15.
    Elmasry, G., Kamruzzaman, M., Sun, D. W., & Allen, P. (2012). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A review. Critical Reviews in Food Science and Nutrition, 52(11), 999–1023. Scholar
  16. 16.
    ElMasry, G., Wang, N., Vigneault, C., Qiao, J., & ElSayed, A. (2008). Early detection of apple bruises on different background colors using hyperspectral imaging. LWT-Food Science and Technology, 41(2), 337–345. Scholar
  17. 17.
    Emadi, A., Wu, H., de Graaf, G., & Wolffenbuttel, R. (2012). Design and implementation of a sub-nm resolution microspectrometer based on a linear-variable optical filter. Optics Express, 20(1), 489–507.CrossRefGoogle Scholar
  18. 18.
    Farrell, J. E., Catrysse, P. B., & Wandell, B. A. (2012). Digital camera simulation. Applied Optics, 51(4), A80–A90. Scholar
  19. 19.
    Farrell, J. E., Xiao, F., Catrysse, P. B., & Wandell, B. A. (2003). A simulation tool for evaluating digital camera image quality. In Electronic imaging 2004 (pp. 124–131). International Society for Optics and Photonics.
  20. 20.
    Folkman, M. A., Pearlman, J., Liao, L. B., & Jarecke, P. J. (2001). Eo-1/hyperion hyperspectral imager design, development, characterization, and calibration. In Second international Asia-Pacific symposium on remote sensing of the atmosphere, environment, and space (pp. 40–51). International Society for Optics and Photonics.
  21. 21.
    Gao, L., Kester, R. T., Hagen, N., & Tkaczyk, T. S. (2010). Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy. Optics Express, 18(14), 14330–14344.CrossRefGoogle Scholar
  22. 22.
    Gat, N. (2000). Imaging spectroscopy using tunable filters: A review. In AeroSense 2000 (pp. 50–64). International Society for Optics and Photonics.
  23. 23.
    Gebhart, S. C., Thompson, R. C., & Mahadevan-Jansen, A. (2007). Liquid-crystal tunable filter spectral imaging for brain tumor demarcation. Applied Optics, 46(10), 1896–1910. Scholar
  24. 24.
    Goetz, A. F. (1995). Imaging spectrometry for remote sensing: Vision to reality in 15 years. In SPIE’s 1995 symposium on OE/aerospace sensing and dual use photonics (pp. 2–13). International Society for Optics and Photonics.
  25. 25.
    Gowen, A., O’Donnell, C., Cullen, P., Downey, G., & Frias, J. (2007). Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology, 18(12), 590–598. Scholar
  26. 26.
    Grabarnik, S., Emadi, A., Wu, H., De Graaf, G., Vdovin, G., & Wolffenbuttel, R. (2008). IC-compatible microspectrometer using a planar imaging diffraction grating. In: Photonics Europe (p. 699215). International Society for Optics and Photonics.Google Scholar
  27. 27.
    Grabarnik, S., Wolffenbuttel, R., Emadi, A., Loktev, M., Sokolova, E., & Vdovin, G. (2007). Planar double-grating microspectrometer. Optics Express, 15(6), 3581–3588.CrossRefGoogle Scholar
  28. 28.
    Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Rogass, C., Chabrillat, S., et al. (2015). The enmap spaceborne imaging spectroscopy mission for earth observation. Remote Sensing, 7(7), 8830–8857.CrossRefGoogle Scholar
  29. 29.
    Gupta, N. (2005). Acousto-optic tunable filters for infrared imaging. In: Congress on optics and optoelectronics (p. 59530O). International Society for Optics and Photonics.
  30. 30.
    Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2), 416–426. Scholar
  31. 31.
    Hackwell, J. A., Warren, D. W., Bongiovi, R. P., Hansel, S. J., Hayhurst, T. L., Mabry, D. J., Sivjee, M. G., & Skinner, J. W. (1996). LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing. In SPIE’s 1996 international symposium on optical science, engineering, and instrumentation (pp. 102–107). International Society for Optics and Photonics.Google Scholar
  32. 32.
    He, Z., Shu, R., & Wang, J. (2011). Imaging spectrometer based on AOTF and its prospects in deep-space exploration application. In International symposium on photoelectronic detection and imaging 2011 (p. 819625). International Society for Optics and Photonics.
  33. 33.
    He, Z., Wang, B., Lv, G., Li, C., Yuan, L., Xu, R., et al. (2014). Visible and near-infrared imaging spectrometer and its preliminary results from the chang’e 3 project. Review of Scientific Instruments, 85(8), 083104. Scholar
  34. 34.
    Hirsch, E., & Agassi, E. (2007). Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering. Applied Optics, 46(25), 6368–6374. Scholar
  35. 35.
    Inoue, Y., & Penuelas, J. (2001). An aotf-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. International Journal of Remote Sensing, 22(18), 3883–3888.CrossRefGoogle Scholar
  36. 36.
    Johnson, W. R., Hook, S. J., Mouroulis, P., Wilson, D. W., Gunapala, S. D., Realmuto, V., Lamborn, A., Paine, C., Mumolo, J. M., & Eng, B. T. (2011). Hytes: Thermal imaging spectrometer development. In 2011 IEEE aerospace conference (pp. 1–8). IEEE.Google Scholar
  37. 37.
    Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science & Emerging Technologies, 16, 218–226. Scholar
  38. 38.
    Khaodhiar, L., Dinh, T., Schomacker, K. T., Panasyuk, S. V., Freeman, J. E., Lew, R., et al. (2007). The use of medical hyperspectral technology to evaluate microcirculatory changes in diabetic foot ulcers and to predict clinical outcomes. Diabetes Care, 30(4), 903–910. Scholar
  39. 39.
    Kim, M. H. (2015). The three-dimensional evolution of hyperspectral imaging. In Y. L. Lin, C. M. Kyung, H. Yasuura, Y. Liu (Eds.) Smart sensors and systems (pp. 63–84). Cham: Springer.CrossRefGoogle Scholar
  40. 40.
    Kim, M. H., Harvey, T. A., Kittle, D. S., Rushmeier, H., Dorsey, J., Prum, R. O., et al. (2012). 3d imaging spectroscopy for measuring hyperspectral patterns on solid objects. ACM Transactions on Graphics (TOG), 31(4), 38.Google Scholar
  41. 41.
    Kittle, D., Choi, K., Wagadarikar, A., & Brady, D. J. (2010). Multiframe image estimation for coded aperture snapshot spectral imagers. Applied Optics, 49(36), 6824–6833.CrossRefGoogle Scholar
  42. 42.
    Kittle, D. S., Marks, D. L., & Brady, D. J. (2012). Design and fabrication of an ultraviolet-visible coded aperture snapshot spectral imager. Optical Engineering, 51(7), 071403–1.CrossRefGoogle Scholar
  43. 43.
    Kleynen, O., Leemans, V., & Destain, M. F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41–49.CrossRefGoogle Scholar
  44. 44.
    Koonen, T. (2006). Fabry–Perot interferometer filters. In Wavelength filters in fibre optics (pp. 271–287). Springer.Google Scholar
  45. 45.
    Kröger, N., Egl, A., Engel, M., Gretz, N., Haase, K., Herpich, I., et al. (2014). Quantum cascade laser-based hyperspectral imaging of biological tissue. Journal of Biomedical Optics, 19(11), 111607–111607.CrossRefGoogle Scholar
  46. 46.
    Kumar, A. K., & Chowdhury, A. R. (2005). Hyper-spectral imager in visible and near-infrared band for lunar compositional mapping. Journal of Earth System Science, 114(6), 721–724. Scholar
  47. 47.
    Kung, C. C., Lee, M. H., & Hsieh, C. L. (2012). Development of an ultraspectral imaging system by using a concave monochromator. Journal of the Chinese Institute of Engineers, 35(3), 329–342.CrossRefGoogle Scholar
  48. 48.
    Kurosaki, H. (2007). Earth observation by the adaptive wavelength optical image sensor. Advances in Space Research, 39(1), 185–189. Scholar
  49. 49.
    Lawrence, K., Park, B., Windham, G. H. W., & Thai, C. (2007). Evaluation of led and tungsten-halogen lighting for fecal contaminant detection. Applied Engineering in Agriculture, 23(6), 811–818.CrossRefGoogle Scholar
  50. 50.
    Lawrence, R. L., Wood, S. D., & Sheley, R. L. (2006). Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (randomforest). Remote Sensing of Environment, 100(3), 356–362. Scholar
  51. 51.
    Leavesley, S. J., Annamdevula, N., Boni, J., Stocker, S., Grant, K., Troyanovsky, B., et al. (2012). Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue. Journal of Biophotonics, 5(1), 67–84.CrossRefGoogle Scholar
  52. 52.
    Li, Q., Xu, D., He, X., Wang, Y., Chen, Z., Liu, H., et al. (2013). Aotf based molecular hyperspectral imaging system and its applications on nerve morphometry. Applied Optics, 52(17), 3891–3901.CrossRefGoogle Scholar
  53. 53.
    Lim, Y. M., Choi, Y. J., Jo, Y. S., Lim, T. H., Ham, J., Min, K., et al. (2013). Hyper-spectral imager of the visible band for lunar observations. Journal of the Korean Physical Society, 62(11), 1587–1590. Scholar
  54. 54.
    Liu, Z., Yu, H., & MacGregor, J. F. (2007). Standardization of line-scan NIR imaging systems. Journal of Chemometrics, 21(3–4), 88–95. Scholar
  55. 55.
    Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142. Scholar
  56. 56.
    Manea, D., & Calin, M. (2015). Hyperspectral imaging in different light conditions. The Imaging Science Journal, 63(4), 214–219.CrossRefGoogle Scholar
  57. 57.
    Messinger, D. W., Salvaggio, C., & Sinisgalli, N. M. (2007). Detection of gaseous effluents from airborne LWIR hyperspectral imagery using physics-based signatures. International Journal of High Speed Electronics and Systems, 17(04), 801–812. Scholar
  58. 58.
    Min, M., Lee, W. S., Burks, T. F., Jordan, J. D., Schumann, A. W., Schueller, J. K., et al. (2008). Design of a hyperspectral nitrogen sensing system for orange leaves. Computers and Electronics in Agriculture, 63(2), 215–226. Scholar
  59. 59.
    Panasyuk, S. V., Yang, S., Faller, D. V., Ngo, D., Lew, R. A., Freeman, J. E., et al. (2007). Medical hyperspectral imaging to facilitate residual tumor identification during surgery. Cancer Biology & Therapy, 6(3), 439–446. Scholar
  60. 60.
    Park, B., Lawrence, K., Windham, W., & Smith, D. (2005). Detection of cecal contaminants in visceral cavity of broiler carcasses using hyperspectral imaging. Applied Engineering in Agriculture, 21(4), 627–635. Scholar
  61. 61.
    Park, B., Windham, W., Lawrence, K., & Smith, D. (2007). Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosystems Engineering, 96(3), 323–333. Scholar
  62. 62.
    Park, B., Yoon, S. C., Windham, W. R., Lawrence, K. C., Kim, M. S., & Chao, K. (2011). Line-scan hyperspectral imaging for real-time in-line poultry fecal detection. Sensing and Instrumentation for Food Quality and Safety, 5(1), 25–32. Scholar
  63. 63.
    Patel, S. R., Flanagan, J. G., Shahidi, A. M., Sylvestre, J. P., & Hudson, C. (2013). A prototype hyperspectral system with a tunable laser source for retinal vessel imaging a prototype hyperspectral system. Investigative Ophthalmology & Visual Science, 54(8), 5163–5168.CrossRefGoogle Scholar
  64. 64.
    Resmini, R., Kappus, M., Aldrich, W., Harsanyi, J., & Anderson, M. (1997). Mineral mapping with hyperspectral digital imagery collection experiment (hydice) sensor data at Cuprite, Nevada, USA. International Journal of Remote Sensing, 18(7), 1553–1570. Scholar
  65. 65.
    Ryan, J. P., Davis, C. O., Tufillaro, N. B., Kudela, R. M., & Gao, B. C. (2014). Application of the hyperspectral imager for the coastal ocean to phytoplankton ecology studies in Monterey Bay, CA, USA. Remote Sensing, 6(2), 1007–1025. Scholar
  66. 66.
    Schaare, P., & Fraser, D. (2000). Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of Kiwifruit (Actinidia chinensis). Postharvest Biology and Technology, 20(2), 175–184. Scholar
  67. 67.
    Senthilkumar, T., Jayas, D., & White, N. (2015). Detection of different stages of fungal infection in stored canola using near-infrared hyperspectral imaging. Journal of Stored Products Research, 63, 80–88. Scholar
  68. 68.
    Thenkabail, P. S., Schull, M., & Turral, H. (2005). Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sensing of Environment, 95(3), 317–341. Scholar
  69. 69.
    Vaughan, R. G., Calvin, W. M., & Taranik, J. V. (2003). Sebass hyperspectral thermal infrared data: Surface emissivity measurement and mineral mapping. Remote Sensing of Environment, 85(1), 48–63. Scholar
  70. 70.
    Wagadarikar, A. A., Pitsianis, N. P., Sun, X., & Brady, D. J. (2009). Video rate spectral imaging using a coded aperture snapshot spectral imager. Optics Express, 17(8), 6368–6388.CrossRefGoogle Scholar
  71. 71.
    Wang, J., He, Z., & Shu, R. (2010). Design and applications of space-borne imaging spectrometer based on acousto-optic tunable filter (AOTF). In SPIE Asia-Pacific remote sensing (p. 78570N). International Society for Optics and Photonics.
  72. 72.
    Wang, W., Li, C., Tollner, E. W., Rains, G. C., & Gitaitis, R. D. (2012). A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration. Computers and Electronics in Agriculture, 80, 126–134. Scholar
  73. 73.
    Windham, W. R., Lawrence, K. C., Park, B., Smith, D. P., & Poole, G. (2002). Analysis of reflectance spectra from hyperspectral images of poultry carcasses for fecal and ingesta detection. In International symposium on optical science and technology (pp. 317–324). International Society for Optics and Photonics.
  74. 74.
    Wu, D., Shi, H., Wang, S., He, Y., Bao, Y., & Liu, K. (2012). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, 726, 57–66. Scholar
  75. 75.
    Wu, D., & Sun, D. W. (2013). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta, 111, 39–46. Scholar
  76. 76.
    Wu, D., Sun, D. W., & He, Y. (2012). Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innovative Food Science & Emerging Technologies, 16, 361–372. Scholar
  77. 77.
    Xu, B., & Gong, P. (2007). Land-use/land-cover classification with multispectral and hyperspectral eo-1 data. Photogrammetric Engineering & Remote Sensing, 73(8), 955–965. Scholar
  78. 78.
    Zhi, L., Zhang, D., Yan, Jq, Li, Q. L., & Tang, Ql. (2007). Classification of hyperspectral medical tongue images for tongue diagnosis. Computerized Medical Imaging and Graphics, 31(8), 672–678. Scholar
  79. 79.
    Zhou, C., & Nayar, S. K. (2011). Computational cameras: Convergence of optics and processing. IEEE Transactions on Image Processing, 20(12), 3322–3340.CrossRefGoogle Scholar
  80. 80.
    Zhu, S., Su, K., Li, M., Chen, Z., Yin, H., & Li, Z. (2016). Multi-type hyper-spectral microscopic imaging system. Optik-International Journal for Light and Electron Optics, 127(18), 7218–7224.CrossRefGoogle Scholar
  81. 81.
    Zimmermann, T., Rietdorf, J., & Pepperkok, R. (2003). Spectral imaging and its applications in live cell microscopy. FEBS Letters, 546(1), 87–92.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Vaibhav Lodhi
    • 1
    Email author
  • Debashish Chakravarty
    • 2
  • Pabitra Mitra
    • 3
  1. 1.Advanced Technology Development CentreIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Department of Mining EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  3. 3.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations