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Correcting and Registering Images

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Remote Sensing Digital Image Analysis

Abstract

When image data is recorded by sensors on satellites and aircraft it can contain errors in geometry, and in the measured brightness values of the pixels. The latter are referred to as radiometric errors and can result from (i) the instrumentation used to record the data (ii) the wavelength dependence of solar radiation and (iii) the effect of the atmosphere.

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Notes

  1. 1.

    This approach is demonstrated in M.P. Weinreb, R. Xie, I.H. Lienesch and D.S. Crosby, Destriping GOES images by matching empirical distribution functions, Remote Sensing of Environment, vol. 29, 1989, pp. 185–195, and M. Wegener, Destriping multiple sensor imagery by improved histogram matching, Int. J. Remote Sensing, vol. 11, no. 5, May 1990, pp. 859–875.

  2. 2.

    See H. Shen and L. Zhang, A MAP-based algorithm for destriping and inpainting of remotely sensed images, IEEE Transactions on Geoscience and Remote Sensing, vol. 47. no. 5, May 2009, pp. 1492–1502, and M. Bouali and S. Ladjal, Towards optimal destriping of MODIS data using a unidirectional variance model, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 8, August 2011, pp. 2924–2935.

  3. 3.

    See N. Acito, M. Diani and G. Corsini, Subspace-based striping noise reduction in hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 4, April 2011, pp. 1325–1342.

  4. 4.

    If the spectral resolution of the detector were sufficiently fine then the recorded solar spectrum would include the Fraunhofer absorption lines associated with the gases in the solar atmosphere: See P.N. Slater, Remote Sensing: Optics and Optical Systems, Addison Wesley, Reading Mass., 1980.

  5. 5.

    See ACORN 4.0 Users Guide, Stand Alone Version, Analytical Imaging and Geophysics LLC, Boulder, Colorado, 2002.

  6. 6.

    See Appendix B.

  7. 7.

    B.C. Forster, Derivation of atmospheric correction procedures for Landsat MSS with particular reference to urban data. Int. J. Remote Sensing, vol. 5, no. 5, 1984, pp. 799–817.

  8. 8.

    R.E. Turner and M.M. Spencer, Atmospheric model for the correction of spacecraft data, Proc. 8th Int. Symposium on Remote Sensing of the Environment, Ann Arbor, Michigan, 1972, pp. 895–934.

  9. 9.

    L.S. Rothman and 42 others, The HITRAN 2008 molecular spectroscopic database, J. Quantitative Spectroscopy and Radiative Transfer, vol. 110, 2009, pp. 533–572.

  10. 10.

    See www.cfa.harvard.edu/HITRAN/.

  11. 11.

    B.C. Gao, K.B. Heidebrecht and A.F.H. Goetz, Derivation of scaled surface reflectance from AVIRIS data, Remote Sensing of Environment, vol. 44, 1993, pp. 165–178.

  12. 12.

    Many correction methodologies use the narrow band transmittance model in W. Malkmus, Random Lorentz band model with exponential-tailed S line intensity distribution function, J. Optical Society of America, vol. 57, 1967, pp. 323–329.

  13. 13.

    Atmosphere Removal Program (ATREM), Version 3.1 Users Guide, Centre for the Study of Earth from Space, University of Colorado, 1999.

  14. 14.

    D. Tanre, C. Deroo, P. Duhaut, M. Herman, J.J. Morchrette, J. Perbos and P.Y. Deschamps, Simulation of the Satellite Signal in the Solar Spectrum (5S) Users Guide, Laboratoire d’Optique Atmospherique, Universitat S.T. de Lille, 1986, and http://6s.ltdri.org/index.html (6S users site).

  15. 15.

    A. Berk, G.P. Anderson, L.S.Bernstein, P.K. Acharya, H. Dothe, M.W. Matthew, S.M. Adler-Golden, J.H. Chetwynd, Jr., S.C. Richtsmeier, B. Pukall, C.L. Allred, L.S. Jeong, and M.L. Hoke, MODTRAN4 Radiative Transfer Modeling for Atmospheric Correction, Proc. SPIE Optical Stereoscopic Techniques and Instrumentation for Atmospheric and Space Research III, vol. 3756, July 1999.

  16. 16.

    ACORN 4.0 Users Guide, Stand Alone Version, loc. cit.

  17. 17.

    S.M. Alder-Golden, M.W. Matthew, L.S. Bernstein, R.Y. Levine, A. Berk, S.C. Richtsmeier, P.K. Acharya, G.P. Anderson, G. Felde, J. Gardner, M. Hike, L.S. Jeong, B. Pukall, J. Mello, A. Ratkowski and H.H. Burke, Atmospheric correction for short wave spectral imagery based on MODTRAN4, Proc. SPIE Imaging Spectrometry, vol. 3753, 1999, pp. 61–69.

  18. 18.

    F.A. Kruse, Comparison of ATREM, ACORN and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO, Proc. 13th JPL Airborne Geoscience Workshop, Pasadena, CA, 2004.

  19. 19.

    B.C Gao, M.J. Montes, C.O. Davis and A.F.H. Goetz, Atmospheric correction algorithms for hyperspectral remote sensing data of land and oceans, Remote Sensing of Environment, Supplement 1, Imaging Spectroscopy Special Issue, vol. 113, 2009, pp. S17–S24.

  20. 20.

    D.A. Roberts, Y. Yamaguchi and R.J.P. Lyon, Comparison of various techniques for calibration of AIS data, Proc. 2nd AIS Workshop, JPL Publication 86–35, Jet Propulsion Laboratory, Pasadena CA, 1986.

  21. 21.

    D.A. Roberts, Y. Yamaguchi and R.J.P. Lyon, Calibration of Airborne Imaging Spectrometer data to percent reflectance using field spectral measurements, Proc. 19th Int. Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, 21–25 October 1985.

  22. 22.

    A.A. Green and M.D. Craig, Analysis of Airborne Imaging Spectrometer data with logarithmic residuals, Proc. 1st AIS Workshop, JPL Publication 85–41, Jet Propulsion Laboratory, Pasadena CA, 8–10 April 1985, pp. 111–119.

  23. 23.

    For an extreme example see Fig. 3.1 in G. Camps-Valls and L. Bruzzone, eds., Kernel Methods for Remote Sensing Data Analysis, John Wiley & Sons, Chichester UK, 2009.

  24. 24.

    For Landsat multispectral scanner products this can lead to a maximum displacement in pixel position compared with a perfectly linear scan of about 395 m; see P. Anuta, Geometric correction of ERTS-1 digital MSS data, Information Note 103073, Laboratory for Applications of Remote Sensing, Purdue University West Lafayette, Indiana, 1973.

  25. 25.

    For a recent, comprehensive treatment of image correction and registration see J. Le Moigne, N. S. Netanyahu and R. D. Eastman, eds., Image Registration for Remote Sensing, Cambridge University Press, Cambridge, 2011.

  26. 26.

    In some treatments this is referred to as a radiometric transformation; see Le Moigne et al., loc. cit.

  27. 27.

    An excellent treatment of the problem has been given by S. Shlien, Geometric correction, registration and resampling of Landsat imagery, Canadian J. Remote Sensing, vol. 5, 1979, pp. 74–89. He discusses several possible cubic polynomials that could be used for the interpolation process and demonstrates that the interpolation is a convolution operation.

  28. 28.

    Based on the choice of interpolation polynomial in T.G. Moik, Digital Processing of Remotely Sensed Images, NASA, Washington, 1980.

  29. 29.

    For other methods see Le Moigne et al., loc. cit.

  30. 30.

    This registration exercise was carried out using the Dipix Systems Ltd R-STREAM Software.

  31. 31.

    J.D. Foley, A. van Dam, S.K. Feiner and J.F. Hughes, Computer Graphics: Principles and Practice in C, 2nd ed., Addison-Wesley, Boston, 1995.

  32. 32.

    See Le Moigne et al., loc. cit.

  33. 33.

    See D.I. Barnea and H.F. Silverman, A class of algorithms for fast digital image registration, IEEE Transactions on Computers, vol. C-21, no. 2, February 1972, pp. 179–186, and R. Bernstein, Image Geometry and Rectification, in R.N. Colwell, ed., Manual of Remote Sensing, 2nd ed., Chap. 21, American Society of Photogrammetry, Falls Church, Virginia, 1983.

  34. 34.

    See P.E. Anuta, Spatial registration of multispectral and multitemporal digital imagery using fast Fourier transform techniques, IEEE Transactions on Geoscience Electronics, vol. GE-8, no. 4, October 1970, pp. 353–368.

  35. 35.

    See Le Moigne et al., loc. cit.

  36. 36.

    See B.C. Forster, loc. cit.

  37. 37.

    For some examples see Shlien, loc. cit.

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  1. ANU College of Engineering and Computer Science, Australian National University, Canberra, ACT, 0200, Australia

    John A. Richards

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Richards, J.A. (2013). Correcting and Registering Images. In: Remote Sensing Digital Image Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30062-2_2

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