Skip to main content
SpringerLink
Log in

Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

  • Conference paper
  • First Online:
Pattern Recognition and Image Analysis (IbPRIA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2652))

Included in the following conference series:

Abstract

Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: i) The observed data vector is a linear mixture of the sources (abundance fractions); ii) sources are independent. Concerning hyperspectral data, the first assumption is valid whenever the constituent substances are surface distributed. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper gives evidence that ICA, at least in its canonical form, is not suited to unmix hyperspectral data. We arrive to this conclusion by minimizing the mutual information of simulated hyperspectral mixtures. The hyperspectral data model includes signature variability, abundance perturbation, sensor Point Spread Function (PSF), abundance constraint and electronic noise. Mutual information computation is based on fitting mixtures of Gaussians to the observed data.

This work was supported by the Fundação para a ciência e Tecnologia, under the project POSI/34071/CPS/2000

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Smith, M.O., Johnson, P.E., Adams, J.B.: Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. In: Proc. 15th Lunar and Planetary Sci. Conf., Part 2, Geophys. Res., February 1985, vol. 90, pp. C797–C804 (1985)

    Article  Google Scholar 

  2. Gillespie, A.R., Smith, M.O., Adams, J.B., Willis, S.C., Fisher, A.F., Sabol, D.E.: Interpretation of residual images: Spectral mixture analysis of aviris images, owens valley, california. In: Green, R.O. (ed.) Proc 2nd AVIRIS Workshop, Jpl Publ., June 1990, vol. 90-54, pp. 243–270 (1990)

    Google Scholar 

  3. Lillesand, T., Kiefer, R.: Remote Sensing and Image Interpretation, 3rd edn. John Wiley & Sons, Inc., Chichester (1994)

    Google Scholar 

  4. Vane, G., Green, R., Chrien, T., Enmark, H., Hansen, E., Porter, W.: The airborne visible/infrared imaging spectrometer (aviris). Remote Sens. Environ. 44, 127–143 (1993)

    Article  Google Scholar 

  5. Borel, C.C., Gerstl, S.A.: Nonlinear spectral mixing models for vegetative and soils surface. Remote Sensing of the Environment 47(2), 403–416 (1994)

    Article  Google Scholar 

  6. Common, P., Jutten, C., Herault, J.: Blind separation of sources, part ii: Problem statement. Signal Processing 24, 11–20 (1991)

    Article  Google Scholar 

  7. Common, P.: Independent component analysis: A new concept. Signal Processing 36, 287–314 (1994)

    Article  Google Scholar 

  8. Bayliss, J., Gualtieri, J.A., Cromp, R.: Analysing hyperspectral data with independent component analysis. In: Proc. SPIE, vol. 3240, pp. 133–143 (1997)

    Google Scholar 

  9. Chen, C., Zhang, X.: Independent component analysis for remote sensing study. In: EOS/SPIE Symp. Remote Sensing Conference on Image and Signal Processing for Remote Sensing V, September 20-24, vol. 3871, pp. 150–158 (1999)

    Google Scholar 

  10. Tu, T.M.: Unsupervised signature extraction and separation in hyperspectral images: A noise-adjusted fast independent component analysis approach. Opt. Eng./SPIE 39(4), 897–906 (2000)

    Article  Google Scholar 

  11. Chiang, S.-S., Chang, C.-I., Ginsberg, I.W.: Unsupervised hyperspectral image analysis using independent component analysis. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp, July 24-28 (2000)

    Google Scholar 

  12. Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Machine Intell. 44(3), 381–396 (2002)

    Article  Google Scholar 

  13. Bateson, C., Asner, G., Wessman, C.: Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis. IEEE Trans. Geosci. Remote Sensing 38, 1083–1094 (2000)

    Article  Google Scholar 

  14. Kruse, F.: Spectral identification of image endmembers determined from aviris data. In: Summaries of the VII JPL Airborne Earth Science Workshop (1998)

    Google Scholar 

  15. Boardman, J., Kruse, F.: Automated spectral analysis: a geological example using aviris data, northern grapevine mountains, nevada. In: Proc. 10th Thematic Conference, Geologic Remote Sensing (1994)

    Google Scholar 

  16. Brumbley, C., Chang, C.-I.: An unsupervised vector quantization-based target signature subspace projection approach to classification and detection in unknown background. Pattern Recognition 32, 1161–1174 (1999)

    Article  Google Scholar 

  17. Ren, H., Chang, C.-I.: A generalized orthogonal subspace projection approach to unsupervised multispectral image classification. IEEE Trans. Geosci. Remote Sensing 38(6), 2515–2528 (2000)

    Article  Google Scholar 

  18. Chang, C.-I., Heinz, D.: Subpixel spectral detection for remotely sensed images. IEEE Trans. Geosci. Remote Sensing 38(3), 1144–1159 (2000)

    Article  Google Scholar 

  19. Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 10, 215–234 (1995)

    Google Scholar 

  20. Cardoso, J.: Infomax and maximum likelihood of source separation. IEEE Signal Processing Lett. 4(4), 112–114 (1997)

    Article  Google Scholar 

  21. Hyvarinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)

    Article  Google Scholar 

  22. Parra, L., Mueller, K.-R., Spence, C., Ziehe, A., Sajda, P.: Unmixing hyperspectral data. Advances in Neural Information Processing Systems 12, 942–948 (2000)

    Google Scholar 

  23. McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley & Sons, Inc., Chichester (2000)

    Book  Google Scholar 

  24. Attias, H.: Independent factor analysis. Neural Computation 11(4), 803–851 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, R. Conselheiro Emídio Navarro N 1, edifício 5, 1949-014, Lisboa, Portugal

    José M. P. Nascimento

  2. Instituto Superior Técnico and Instituto de Telecomunicações, Av. Rovisco Pais, Torre Norte, Piso 10, 1049-001, Lisboa, Portugal

    José M. B. Dias

Authors
  1. José M. P. Nascimento
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José M. B. Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco José Perales

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio J. C. Campilho

  3. Departamento de Ciencias da la Computacíon e I.A., Universidad de Granada, E.T. S. Ing. Informática, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  4. Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain

    Alberto Sanfeliu

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nascimento, J.M.P., Dias, J.M.B. (2003). Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_72

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-44871-6_72

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40217-6

  • Online ISBN: 978-3-540-44871-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation