Data Analysis - Processing Requirements and Available Software Tools

  • Wolfgang Mehl
Part of the Eurocourses: Remote Sensing book series (EURS, volume 4)


Methods for analysing remote sensing data are conditioned by sensor technology as much as by the particular application. However, in the past twenty years a number of techniques have proved to be widely applicable and have become standard tools available in public domain or commercially offered software packages. Many of these tools are no longer applicable to data acquired with imaging spectrometers either because of the high dimensionality of the data which increases processing time beyond practical bounds (e. g. maximum likelihood classifiers), or because of the high statistical variability of data which again is directly linked to the data space dimension (e. g. clustering algorithms), or simply because implementations have arbitrary limits for the processable number of bands. New techniques have evolved which exploit the continuity of spectral information available with imaging spectrometers (e. g. correlation and fitting algorithms) or the detail observed in the short wave infrared region. Also techniques which have not been generally accepted in the past have been reconsidered within the context of image spectrometry (e. g. spectral unmixing).


Digital Elevation Model Spectral Band Atmospheric Correction Imaging Spectrometer Maximum Likelihood Classifier 
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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6. References

  1. Colwell, R.N., D.S. Simonett and F.W. Ulaby (eds.) (1983a), ‘Manual of Remote Sensing’, Volume 1: Theory, Instruments and Techniques, American Society of Photogrammetry, Falls Church.Google Scholar
  2. Colwell, R.N., DIE. Estes, and G.A. Thorley (eds.) (1983b), ‘Manual of Remote Sensing’, Volume 2: Interpretation and Applications, American Society of Photogrammetry, Falls Church.Google Scholar
  3. Conel, J.E., R.O. Green, R.E. Alley, C.J. Bruegge, V. Carrere, J.S. Margoslis, G. Vane, T.G. Chrien, P.N. Slater, S.F. Biggar, P.M. Teillet, R.D. Jackson and M.S. Moran (1988) ‘In-flight radiometric calibration of the Airborne Visible/Infrared Imaging Spectrometr (AVIRIS)’, in P.N. Slater (ed.) Recent advantages in sensors, radiometry, and data processing for remote sensing, Proc. SPIE, vol. 924, 179–195.Google Scholar
  4. Crist, E.P., and R.C. Cicone (1984), ‘A physically-based transformation of Thematic Mapper data-the TM tasseled cap’, IEEE Trans. Geoscience & Remote Sensing, vol. GE-22,no. 3, 256–263.CrossRefGoogle Scholar
  5. Gao, B.-C, and A.F.H. Goetz (1990), ‘Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data’, J. Geophys. Res., 95, 3549–3564.CrossRefGoogle Scholar
  6. Gao, B.-C, K. Heidebrecht, and A.F.H. Goetz (1993), ‘Derivation of scaled surface reflectances from AVIRIS data’, Remote Sensing of Environment, vol. 44,nos. 2/3, 165–178.CrossRefGoogle Scholar
  7. Gerig, G., and K. Seidel (1985), ‘Structural description of a LANDSAT TM scene for improved region-based classification’, Proceedings of IGARSS’ 84 Symposium, Straßburg, Aug 27–30, ESA Publication SP-215, 101–105.Google Scholar
  8. Green, R.O., J.E. Conel, C.J. Bruegge, J.S. Margolis, V. Carrere, G. Vane, G. Hoover (1992), ‘In-flight calibration of the spectral and radiometric characteristics of AVIRIS’, in R.O. Green (ed.) Summaries of the Third Annual JPL Airborne Geoscience Workshop June 1–5, 1992, JPL Publication 92-14, vol. 1, 1–4.Google Scholar
  9. Kanellopoulos, I., A. Varfis, G.G. Wilkinson, and J. Mégier (1992), ‘Land-cover discrimination in SPOT HRV imagery using an artificial neural network-a 20-class experiment’, Int. J. Remote Sensing, vol. 13,no. 5, 917–924.CrossRefGoogle Scholar
  10. Hill, J. (1990), ‘Radiometric comparison and calibration of remotely sensed data from polar-orbiting earth observation satellites’, Proc. 5th Australasian Remote Sensing Conference, Perth, Oct 8–12, 1990, vol. 1, 42–53.Google Scholar
  11. Hill, J., W. Mehl, and J. Mégier (1993), ‘Analysis of GER imaging spectrometry data for the identification of soil and vegetation parameters in land degradation studies’, Proceedings of the final EISAC Workshop, Ispra 1991, ESA Publication SP-360, 27–33.Google Scholar
  12. Nagao, M, and T. Matsuyama (1979), ‘A structural analysis of complex aerial photographs’, Plenum Press, New York.Google Scholar
  13. Nilsson N.J. (1965), ‘Learning Machines’, McGraw-Hill, New York.Google Scholar
  14. Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling (1986), ‘Numerical Recipes: The Art of Scientific Computing (FORTRAN and PASCAL Edition)’, Cambridge University Press, Cambridge, New York, Melbourne.Google Scholar
  15. Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling (1988), ‘Numerical Recipes in C: The Art of Scientific Computing’, Cambridge University Press, Cambridge, New York, Melbourne.Google Scholar
  16. Tom, V.T. (1986), ‘A synergistic approach for multispectral image restoration using reference imagery’, Proc. of the IGARSS’ 86 Symposium, Zurich, Sep 8–11, ESA Publication SP-254, 559–564.Google Scholar

Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1994

Authors and Affiliations

  • Wolfgang Mehl
    • 1
  1. 1.Institute for Remote Sensing ApplicationsCommission of the European Communities Joint Research CentreIspra (Va)Italy

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