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Correcting and Registering Images

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Remote Sensing Digital Image Analysis

Abstract

Sources of error and distortion in the recorded brightness values and in the geometry of remote sensing imagery are presented, along with detailed methods for their correction. Particular attention is given to the effect of the earth’s atmosphere on recorded image data and those atmospheric constituents that have most influence. The use of control points and mapping functions to correct geometric errors is covered in detail, including as a means for registering images to a map base and to register sets of images to each other geographically. Mathematical models for common sources of geometric distortion are also treated. Examples of the main methods for correction and registration are given.

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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 in painting 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.

    Plotted, at lower spectral resolution, from the data in F. X. Kneizys, E. P. Shettle, L. W. Abreu, J. H. Chetwynd, G. P. Anderson, W. O. Gallery, J. E. A. Selby and S. A. Clough, Users Guide to LOWTRAN 7, AFGL-TR-0177, Environmental Research Paper No. 1010, 1988.

  6. 6.

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

  7. 7.

    See Appendix B.

  8. 8.

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

  9. 9.

    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.

  10. 10.

    D. Frantz, A. Roder, M. Stellmes and J. Hill, An operational radiometric Landsat pre-processing framework for large-area time series applications, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 7 2016, pp. 3928–3943.

  11. 11.

    E. Vermote, J-C Roger, B. Franch and S. V. Skakun, LaSRC (Land Surface Reflectance Code): Overview, application and validation using MODIS, VIIRS, Landsat and Sentinel-2 data, Proc. International Geoscience and Remote Sensing Symposium, Valencia, July 2018, pp. 8173–8176.

  12. 12.

    B. Petrucci, M. Huc, T. Feuvrier, C. Ruffel, O. Hagolie, V. Lonjou and C. Dejardins, MACCS: Multi-Mission Atmospheric Correction and Cloud Screening tool for high-frequency revisit data processing, Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 964307, October 2015, https://doi.org/10.1117/12.2194797.

  13. 13.

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

  14. 14.

    See www.cfa.harvard.edu/HITRAN/ accessed April 2021.

  15. 15.

    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.

  16. 16.

    Adapted from Figs. 2 and 3 of B. Gao, K. B. Heidebrecht and A. F. H. Goetz, ibid; used with permission of Elsevier.

  17. 17.

    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.

  18. 18.

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

  19. 19.

    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, E. F. Vermote, D. Tanre, J. L. Deuze, M. Herman, J-J Morc and J-J Morcretee, Second simulation of the satellite signal in the solar spectrum, 6S: An overview, IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 3 1997, pp. 675–686, and https://salsa.umd.edu/6spage.html (the 6S users site).

  20. 20.

    S. Y. Kotchenova, E. F. Vermote, R. Matarrese, and F. J. Klemm, Jr, Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data, Part 1: path radiance, Applied Optics, Vol. 45, Issue 26, 2006, pp. 6762–6774 and S. Y. Kotchenova and E. F. Vermote, Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data, Part 2: Homogeneous, Lambertian and anisotropic surfaces, Applied Optics, Vol. 46, Issue 20, 2007, pp. 4455–4464.

  21. 21.

    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.

  22. 22.

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

  23. 23.

    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.

  24. 24.

    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.

  25. 25.

    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.

  26. 26.

    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.

  27. 27.

    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 1986.

  28. 28.

    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.

  29. 29.

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

  30. 30.

    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.

  31. 31.

    For a 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.

  32. 32.

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

  33. 33.

    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.

  34. 34.

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

  35. 35.

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

  36. 36.

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

  37. 37.

    K. R. Castleman, Digital Image Processing, 2nd ed., Prentice Hall, N.J., 1996.

  38. 38.

    See Le Moigne et al., loc. cit. and T. T. Nguyen, Optimal ground control points for geometric correction using genetic algorithm with global accuracy, European J. Remote Sensing, vol. 47, no. 1, 2015, pp. 101–120 (which contains a review of automated techniques).

  39. 39.

    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 Registration, in R. N. Colwell, ed., Manual of Remote Sensing, 2nd ed., Chap. 21, American Society of Photogrammetry, Falls Church, Virginia, 1983.

  40. 40.

    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.

  41. 41.

    See Le Moigne et al., loc. cit.

  42. 42.

    See B. C. Forster, loc. cit.

  43. 43.

    For some examples see Shlien, loc. cit.

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

    John A. Richards

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Richards, J.A. (2022). Correcting and Registering Images. In: Remote Sensing Digital Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-82327-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-82327-6_2

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