Skip to main content

Advertisement

Log in

Web-Based Interaction and Visualization of Spectral Reflectance Images: Application to Vegetation Inspection

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Visualization of spectral images and interaction with them is still a challenge. We demonstrate an edge-computing, web-technology based solution to handle spectral mage data and allow real-time interaction with it. The solution is flexible, efficient and applicable. It includes visualization strategies based on color science, image processing and statistics. An example of use is provided through a collaboration with a domain knowledge expert for an application related to vegetation inspection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. https://threejs.org

  2. https://www.couleur.org/articles/SN-ComputerScience2021/pca-v2.html?image=data/specimiq-1.pca&dim=30

References

  1. Multispectral image formats, cie 223:2017, isbn: 978-3-902842-10-7, cie division 8. http://www.cie.co.at/publications/multispectral-image-formats

  2. Agarwal A, El-Ghazawi T, El-Askary H, Le-Moigne J. Efficient hierarchical-pca dimension reduction for hyperspectral imagery. 2007; p. 353–6.

  3. Akhtar N, Ajmal M. Hyperspectral recovery from rgb images using gaussian processes. IEEE Trans Pattern Anal Mach Intell. 2018.

  4. Akima H. A new method of interpolation and smooth curve fitting based on local procedures. J ACM. 1970;17(4):589–602.

    Article  Google Scholar 

  5. Amankwah A, Aldrich C. A spatial information measure method for hyperspectral image visualization. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015; p. 4542–5.

  6. Behmann J, Acebron K, Emin D, Bennertz S, Matsubara S, Thomas S, Bohnenkamp D, Kuska MT, Jussila J, Salo HAMA, Rascher U. Specim iq: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors 2018;18(2):441.

    Article  Google Scholar 

  7. Blerot B, Baudino S, Prunier C, Demarne F, Toulemonde B, Caissard J. Botany, agronomy and biotechnology of pelargonium used for essential oil production. Phytochem Rev. 2015;15:935–60.

    Article  Google Scholar 

  8. Brauers J, Schulte N, Aach T. Multispectral filter-wheel cameras: Geometric distortion model and compensation algorithms. IEEE Trans Image Process. 2008;17(12):2368–80.

    Article  MathSciNet  Google Scholar 

  9. Bühler J, Rishmawi L, Pflugfelder D, Huber G, Scharr H, Hülskamp M, Koornneef M, Schurr U, Jahnke S. phenovein-a tool for leaf vein segmentation and analysis. Plant Physiol. 2015;169(4):2359–70. https://doi.org/10.1104/pp.15.00974.

    Article  Google Scholar 

  10. Buschmann C, Lenk S, Lichtenthaler HK. Reflectance spectra and images of green leaves with different tissue structure and chlorophyll content. Israel J Plant Sci. 2012;60(1–2):49–64. https://doi.org/10.1560/IJPS.60.1-2.49.

    Article  Google Scholar 

  11. Choudhary B, KumarSinha N, Shanker P. Pyramid method in image processing. J Inf Syst Commun. 2012;3(1):269–73.

    Google Scholar 

  12. Colantoni P, Thomas JB. A color management process for real time color reconstruction of multispectral images. In: Salberg AB, Hardeberg JY, Jenssen R, editors. Image Analysis. Berlin: Springer; 2009. p. 128–37.

    Chapter  Google Scholar 

  13. Colantoni P, Thomas JB, Hardeberg JY. High-end colorimetric display characterization using an adaptive training set. J Soc Inf Disp. 2011;19(8):520–30. https://doi.org/10.1889/JSID19.8.520.

    Article  Google Scholar 

  14. Colantoni P, Thomas JB, Hebert M, Trémeau A. An online tool for displaying and processing spectral reflectance images. In: 2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), 2019. p. 725–31.

  15. Colantoni P, Thomas JB, Pillay R. Graph-based 3d visualization of color content in paintings. In: A. Artusi, M. Joly, G. Lucet, D. Pitzalis, A. Ribes (eds.) VAST: International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage - Short and Project Papers. The Eurographics Association. 2010. https://doi.org/10.2312/PE/VAST/VAST10S/025-030.

  16. Colantoni P, Thomas JB, Trémeau A, Hardeberg JY. Web technologies enable agile color management. In: 2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), 2019. p. 303–10.

  17. Colantoni P, Trèmeau A. Web browsers colorimetric characterization. 2019. p. 145–61.

  18. Cucci C, Delaney JK, Picollo M. Reflectance hyperspectral imaging for investigation of works of art: Old master paintings and illuminated manuscripts. Acc Chem Res. 2016;49:2070–9.

    Article  Google Scholar 

  19. Feng L, Zhang Y, Li M, Zheng Y, Shen W, Jiang L. The structural color of red rose petals and their duplicates. Langmuir. 2010;26(18):14885–8. https://doi.org/10.1021/la102406u.

    Article  Google Scholar 

  20. Gkikas D, Argiropoulos A, Rhizopoulou S. Epidermal focusing of light and modelling of reflectance in floral-petals with conically shaped epidermal cells. Flora - Morphol Distrib Funct Ecol Plants. 2015;212:38–45. https://doi.org/10.1016/j.flora.2015.02.005http://www.sciencedirect.com/science/article/pii/S0367253015000110.

    Article  Google Scholar 

  21. Hardeberg J, Schmitt F, Brettel H. Multispectral color image capture using a liquid crystal tunable filter. Opt Eng. 2002;41(10):2532–48.

    Article  Google Scholar 

  22. Imai FH, Rosen MR, Berns RS. Comparative study of metrics for spectral match quality. 2002. p. 492–6.

  23. Khan HA, Thomas JB, Hardeberg JY, Laligant O. Spectral adaptation transform for multispectral constancy. J Imaging Sci Technol 2018;62(2), 20504–1–12.

  24. Khan HA, Thomas JB, Hardeberg JY, Laligant O. Multispectral camera as spatio-spectrophotometer under uncontrolled illumination. Opt Express. 2019;27(2):1051–70. https://doi.org/10.1364/OE.27.001051.

    Article  Google Scholar 

  25. Kim SJ, Zhuo S, Deng F, Fu C, Brown M. Interactive visualization of hyperspectral images of historical documents. IEEE Trans Visual Comput Graph. 2010;16(6):1441–8.

    Article  Google Scholar 

  26. van der Kooi CJ, Elzenga JTM, Staal M, Stavenga DG. How to colour a flower: on the optical principles of flower coloration. Proc R Soc B: Biol Sci. 2016;283(1830):20160429. https://doi.org/10.1098/rspb.2016.0429.

    Article  Google Scholar 

  27. Kotwal K, Chaudhuri S. Visualization of hyperspectral images using bilateral filtering. IEEE Trans Geosci Remote Sens. 2010;48(5):2308–16.

    Article  Google Scholar 

  28. Lapray PJ, Wang X, Thomas JB, Gouton P. Multispectral filter arrays: recent advances and practical implementation. Sensors. 2014;14(11):21626–59.

    Article  Google Scholar 

  29. Le Moan SAM, Voisin Y, Hardeberg JY. A constrained band selection method based on information measures for spectral image color visualization. IEEE Trans Geosci Remote Sens. 2011;49(12):5104–15.

    Article  Google Scholar 

  30. Liao D, Chen S, Qian Y. Visualization of hyperspectral images using moving least squares. In: 2018 24th International Conference on Pattern Recognition (ICPR), p. 2851–6.

  31. Liao, D., Qian, Y., Zhou, J.: Visualization of hyperspectral imaging data based on manifold alignment. In: 22nd International Conference on Pattern Recognition, 2014. p. 70–75.

  32. Liu D, Wang L, Atli Benediktsson J. Interactive multi-image colour visualization for hyperspectral imagery. Int J Remote Sens. 2017;38(4):1062–82. https://doi.org/10.1080/01431161.2016.1277041.

    Article  Google Scholar 

  33. Martinez K, Cupitt J, Perrya S. High resolution colorimetric image browsing on the web. Comput Netw ISDN Syst. 1998;30:399–405.

    Article  Google Scholar 

  34. Mokrzycki W, Tatol M. Color difference delta e-a survey. Mach Graph Vis. 2011;20:383–411.

    Google Scholar 

  35. Nguyen RMH, Prasad DK, Brown MS. Training-based spectral reconstruction from a single rgb image, lecture notes in computer science. 2014. p. 186–201.

  36. Pardo A, Gutiérrez-Gutiérrez JAAHJM, Conde OM. Context-free hyperspectral image enhancement for wide-field optical biomarker visualization. Biomed Opt Express. 2020;11(1):133–48.

  37. Polder G, van der Heijden GW. Visualization of spectral image. In: Censor Y, Ding M, editors. Visualization and Optimization Techniques, vol. 4553. SPIE: International Society for Optics and Photonics; 2001. p. 132–7.

    Chapter  Google Scholar 

  38. Pyke KA, Page AM. Plastid ontogeny during petal development in arabidopsis. Plant Physiology. 1998;116(2):797–803. https://doi.org/10.1104/pp.116.2.797.

    Article  Google Scholar 

  39. Ribés A, Brettel H, Schmitt FJM, Liang H, Cupitt J, Saunders D. Color and multispectral imaging with the crisatel multispectral system. 2003. p. 215–219.

  40. RoBler F, Botchen RP, Ertl T. Dynamic shader generation for flexible multi-volume visualization. 2008. p. 17–24.

  41. Sánchez Sorzano C, Vargas J, Pascual-Montano A. A survey of dimensionality reduction techniques. 2014.

  42. Schockling M, Bonce R, Gutierrez, A, Robila SA. Visualization of hyperspectral images. In: S.S. Shen, P.E. Lewis (eds.) Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, vol. 7334, p. 715–26. SPIE. 2009.

  43. Su H, Du Q, Du P. Hyperspectral image visualization using band selection. IEEE J Select Top Appl Earth Obser Remote Sens. 2014;7(6):2647–58.

    Article  Google Scholar 

  44. Thomas JB. Multispectral imaging for computer vision. In: Habilitation à diriger des recherches. Université de Bourgogne, Franche-Comté. 2018. http://jbthomas.org/Thesis/2018HDRThesisCompactVersion.pdf

  45. Thomas JB, Hardeberg JY. How to look at spectral images? a tentative use of metameric black for spectral image visualisation. In: J.B. Thomas, G.C. Guarnera, S. George, P. Nussbaum, S.A. Amirshahi, V. Kitanovski (eds.) Proceedings of the 10th Colour and Visual Computing Symposium 2020 (CVCS 2020), 2020;2688, p. 1–11.

  46. Thomas JB, Hardeberg JY, Foucherot I, Gouton P. The PLVC display color characterization model revisited. Color Res Appl. 2008;33(6):449–60. https://doi.org/10.1002/col.20447.

    Article  Google Scholar 

  47. Wazir ZK, Ejaz A, Saqib H, Ibrar Y, Arif A. Edge computing: A survey. Fut Gen Comput Syst. 2019;97:219–35. https://doi.org/10.1016/j.future.2019.02.050. http://www.sciencedirect.com/science/article/pii/S0167739X18319903.

    Article  Google Scholar 

  48. Wilts B, Rudall P, Moyroud E, Gregory T, Ogawa Y, Vignolini S, Steiner U, Glover B. Ultrastructure and optics of the prism-like petal epidermal cells of eschscholzia californica (california poppy). New Phytol. 2018;219. https://doi.org/10.1111/nph.15229.

  49. Xudong K, Puhong D, Shutao L. Hyperspectral image visualization with edge-preserving filtering and principal component analysis. Inf Fusion. 2020;57:130–43.

    Article  Google Scholar 

Download references

Funding

Funded by the Fondation de l’Université Jean Monnet de Saint-Etienne.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philippe Colantoni.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances on Signal Image Technology and Internet based Systems” guest edited by Albert Dipanda, Luigi Gallo and Kokou Yetongnon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Colantoni, P., Thomas, JB., Hébert, M. et al. Web-Based Interaction and Visualization of Spectral Reflectance Images: Application to Vegetation Inspection. SN COMPUT. SCI. 3, 12 (2022). https://doi.org/10.1007/s42979-021-00870-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00870-8

Keywords