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Spectral Mixture Analysis - New Strategies for the Analysis of Multispectral Data

  • Milton O. Smith
  • John B. Adams
  • Don E. Sabol
Part of the Eurocourses: Remote Sensing book series (EURS, volume 4)

Abstract

Instrument noise, spectral contrast among scene components and variability of spectral scene components are not explicitly evaluated as part of classification and mapping efforts using multispectral images. Yet changes in these factors directly affect mapping accuracy. An analytical framework is proposed such that these factors can be quantified within the context of spectral mixture analysis (SMA). In applying these analyses to an AVBRIS image of Owens Valley, California, U.S.A., we find that the greatest uncertainty in abundance estimates arises from spectral variability in endmembers. Spectral variability in any endmember results in abundance uncertainty of all endmembers. We propose an analytical strategy that subsets an image into regions of lowest spectral dimensionality to minimize uncertainties and to maximize detection of new materials.

Keywords

Multispectral Image Abundance Estimate Instrumental Noise Spectral Variability Linear Mixture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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7. References

  1. Bevington, P.R. (1969) ‘Data Reduction and Error Analysis for the Physical Sciences’, McGraw-Hill Book Company, New York, N.Y.Google Scholar
  2. Gillespie, A.R., M.O. Smith, J.B. Adams, S.C. Willis, A.F. Fischer, and D.E. Sabol (1990) ‘Interpretation of residual images: spectral mixture analysis of AVIRIS images, Owens Valley, California’, Proceedings of the Airborne Science Workshop, JPL Publ., 90-54, 243–270.Google Scholar
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  4. Roberts, D.A., M.O. Smith, D.E. Sabol, J.B. Adams, and S.L. Ustin (1992) ‘Mapping the spectral variability in photosynthetic and non-photosynthetic vegetation, soils and shade using AVIRIS’in R.O. Green (ed.), ‘Summaries of the Third Annual JPL Airborne Geoscience Workshop’, vol. 1, AVIRIS Workshop, June 1–2,1992. pp. 38–40.Google Scholar
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  7. Sabol, D.E., D.A. Roberts, M.O. Smith, and J.B. Adams (1992b) ‘Temporal variation in spectral detection thresholds of substrate and vegetation in AVIRIS images’, in R.O. Green (ed.), ‘Summaries of the Third Annual JPL Airborne Geoscience Workshop’, vol. 1, AVIRIS Workshop, June 1–2,1992. pp. 132–134.Google Scholar
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  9. Smith, M.O., J.B. Adams, and A.R. Gillespie (1990b) ‘Reference endmembers for spectral mixture analysis’, Proc. 5th Australian Remote Sensing Conference, Perth, Western Australia, 8–12 October, vol. 1, 331–340.Google Scholar

Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1994

Authors and Affiliations

  • Milton O. Smith
    • 1
  • John B. Adams
    • 1
  • Don E. Sabol
    • 1
  1. 1.Department of Geological Sciences AJ-20University of Washington SeattleWashingtonUSA

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