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Iberian Conference on Pattern Recognition and Image Analysis

IbPRIA 2005: Pattern Recognition and Image Analysis pp 207–214Cite as

Signal Subspace Identification in Hyperspectral Linear Mixtures

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

Abstract

Hyperspectral applications in remote sensing are often focused on determining the so-called spectral signatures, i.e., the reflectances of materials present in the scene (endmembers) and the corresponding abundance fractions at each pixel in a spatial area of interest. The determination of the number of endmembers in a scene without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper proposes a new mean squared error approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense.

This work was supported by the FCT, under the projects POSI/34071/CPS/2000 and PDCTE/CPS/49967/2003 and by DEETC of ISEL.

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References

  1. Keshava, N., Mustard, J.: Spectral unmixing. IEEE Sig. Proc. Mag. 19, 44–57 (2002)

    Article  Google Scholar 

  2. Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Rem. Sens. and Image Interp, 5th edn. John Wiley & Sons, Inc., Chichester (2004)

    Google Scholar 

  3. Vane, G., Green, R., Chrien, T., Enmark, H., Hansen, E., Porter, W.: The airborne visible/infrared imaging spectrometer (AVIRIS). Rem. Sens. of the Environ. 44, 127–143 (1993)

    Article  Google Scholar 

  4. Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice Hall, N.J., Englewood Cliffs (1988)

    MATH  Google Scholar 

  5. Jolliffe, I.T.: Principal Component Analysis. Spriger, New York (1986)

    Google Scholar 

  6. Green, A., Berman, M., Switzer, P., Craig, M.D.: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Rem. Sens. 26, 65–74 (1994)

    Article  Google Scholar 

  7. Lee, J.B., Woodyatt, S., Berman, M.: Enhancement of high spectral resolution remote-sensing data by noise-adjusted principal components transform. IEEE Trans. Geosci. Rem. Sens. 28, 295–304 (1990)

    Article  Google Scholar 

  8. Bruske, J., Sommer, G.: Intrinsic dimensionality estimation with optimaly topologic preserving maps. IEEE Trans. PAMI. 20, 572–575 (1998)

    Google Scholar 

  9. Demartines, P., Hérault, J.: Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Trans. Neural Networks 8, 148–154 (1997)

    Article  Google Scholar 

  10. Lennon, M., Mercier, G., Mouchot, M., Hubert-Moy, L.: Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images. In: Proc. of the SPIE., vol. 4541 (2001)

    Google Scholar 

  11. Chang, C.I.: Hyperspectral Imaging: Techniques for spectral detection and classification. Kluwer Academic, New York (2003)

    Google Scholar 

  12. Scharf, L.L.: Statistical Signal Processing. Detection Estimation and Time Series Analysis. Addison-Wesley Pub. Comp, Reading (1991)

    MATH  Google Scholar 

  13. Manolakis, D., Siracusa, C., Shaw, G.: Hyperspectral subpixel target detection using linear mixing model. IEEE Trans. Geosci. Rem. Sens. 39, 1392–1409 (2001)

    Article  Google Scholar 

  14. Roger, R., Arnold, J.: Reliably estimating the noise in aviris hyperspectral imagers. International J. of Rem. Sens. 17, 1951–1962 (1996)

    Article  Google Scholar 

  15. Clark, R.N., Swayze, G.A., Gallagher, A., King, T.V., Calvin, W.M.: The u.s.g.s. digital spectral library: Version 1: 0.2 to 3.0 μm. Open file report 93-592, U.S.G.S. (1993)

    Google Scholar 

  16. Swayze, G., Clark, R., Sutley, S., Gallagher, A.: Ground-truthing aviris mineral mapping at cuprite, nevada. In: Summaries of the Third Annual JPL Airborne Geosciences Workshop, vol. 1, pp. 47–49 (1992)

    Google Scholar 

  17. Ashley, R., Abrams, M.: Alteration mapping using multispectral images - cuprite mining district, esmeralda county. Open file report 80-367, U.S.G.S. (1980)

    Google Scholar 

  18. Abrams, M., Ashley, R., Rowan, L., Goetz, A., Kahle, A.: Mapping of hydrothermal alteration in the cuprite mining district, nevada, using aircraft scanner images for the spectral region 0.46 to 2.36mm. Geology 5, 713–718 (1977)

    Article  Google Scholar 

  19. Goetz, A., Strivastava, V.: Mineralogical mapping in the cuprite mining district. In: Proc. of the Airborne Imaging Spectrometer Data Analysis Workshop, pp. 22–29. JPL Publication 85-41 (1985)

    Google Scholar 

  20. Kruse, F., Boardman, J., Huntington, J.: Comparison of airborne and satellite hyperspectral data for geologic mapping. In: Proc. of SPIE, vol. 4725, pp. 128–139 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, R. Conselheiro Emídio Navarro N 1, edifício DEETC, 1950-062, 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
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  2. José M. B. Dias
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica,, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. Instituto Superior Técnico, CERENA-Centro de Recursos Naturais e Ambiente, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Nascimento, J.M.P., Dias, J.M.B. (2005). Signal Subspace Identification in Hyperspectral Linear Mixtures. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_26

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  • DOI: https://doi.org/10.1007/11492542_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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