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Digital Image Processing

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Abstract

Methodologies used in Remote Sensing (RS) directly and indirectly interact with other techniques such as image analysis and pattern recognition.

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Further Reading

  • AAVV, 1997, Manual of Photographic Interpretation, Philipson W.R. (Editor in Chief), 2nd ed. American Society for Photogrammetry and Remote Sensing. Bethesdo, Maryland

    Google Scholar 

  • Avery T.E., Berlin G.L., 1992, Fundamentals of Remote Sensing and Airphoto Interpretation, Fifth ed. Macmillan Publishing Company, New York, p. 472.

    Google Scholar 

  • Chen C.H. (Ed.), 1996, Fuzzy Logic and Neural Network Handbook. McGraw-Hill Professional Publishing Group. New York.

    Google Scholar 

  • Lenoble J., 1985, Radiative Transfer in Scattering and Absorbing Atmospheres: Standard Computational Procedures. A. Deepak Publishing, p. 583, ISBN 0-12-451451-0. http://www.deepakonline. com.

  • Oosterom P.V., Zlatanova S., Penninga F., Fendel, E. (Eds.), 2008, Advanced in 3D Geoinformation Systems. Springer, ISBN 978-3-540-72134-5. Berlin.

    Google Scholar 

  • Richards J.A., Xiuping J., 1998, Remote Sensing Digital Image Analysis, An Introduction, 3rd revisited and enlarged edition. Springer. Berlin.

    Google Scholar 

Bibliography

  • Aschbacher J., 1995a, Rice mapping and crop growth monitoring. Earth Observation Quarterly, ESA, 49: 1–3.

    Google Scholar 

  • Aschbacher J., Pongsrihadulchai, A. et al., 1995b, Assessment of ERS-1 SAR data for rice crop mapping and monitoring. IEEE Transaction on Geoscience and Remote Sensing, 2: 2183–2185.

    Google Scholar 

  • Avery T.E., Berlin G.L., 1985, Interpretation of Aerial Photographs. Burgess Publishing Company, Minneapolis, USA, 4th Edition.

    Google Scholar 

  • Baret F., Guyot G., 1991, Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35: 161–173.

    Article  Google Scholar 

  • Baret F., Guyot G., Major D.J., 1989, TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing and IGARSS’89, Vancouver, Canada, Vol. 3, pp. 1355–1358.

    Google Scholar 

  • Bowers S.A., Hanks R.J., 1965, Reflection of radiant energy from soil. Soil Science, 100: 130–138.

    Article  Google Scholar 

  • Bocchi S., Gomarasca M.A., Maggiore T., Galli A., Rossi B., 1995, Rice (Oryza sativa L.) growth analysis using radiometric indices. International Journal of Agricoltura Mediterranea, 125: 341–353, n. RAISA 900.

    Google Scholar 

  • Brigatti M., Fasolini D., Gomarasca M.A., Pagnoni F., Rasio R., 1995, Interaction of soil information system and remote sensing for identification of land use in relevant soil mapping units. International Symposium Remote Sensing and GIS as Tools for Monitoring Soils in the Environment, 6–10 February 1995, Ouagadougou, Burkina Faso, pp. 351–361, n. RAISA 2013.

    Google Scholar 

  • Clevers J.G.P.W., van Leeuwen H.J.C., 1996, Combined use of optical and microwave remote sensing data for crop growth monitoring. Remote Sensing of Environment, 56(1): 42–50.

    Article  Google Scholar 

  • Colwell B.J., 1974, Vegetation canopy reflectance. Remote Sensing of Environment, 3: 175–183.

    Article  Google Scholar 

  • Crist E.P., Cicone R.C., 1984, A physically-based transformation of thematic mapper data: the Tasseled Cap. IEEE Transactions, v. GE 22, 3: 256–263.

    Google Scholar 

  • Elvidge C.D., Chen Z., 1995, Comparison of broadband and narrow-band red and near infrared vegetation indices. Remote Sensing of Environment, 54: 38–48.

    Article  Google Scholar 

  • ESA, European Space Agency, 1995a, Satellite Radar in Agriculture, Experience with ERS-01, SP-1185, Eds. ESTEC, Noordwijk, The Netherlands, pp. 1–71.

    Google Scholar 

  • ESA, European Space Agency, 1995b, New View of the Earth, Scientific Achievements of ERS-1, ESA SP-1176-I, Eds. ESTEC, Noordwijk, The Netherlands, pp. 1–162.

    Google Scholar 

  • ESA, European Space Agency, 1996, New View of the Earth, Scientific Achievements of ERS-1, ESA SP-1176-II, Eds. ESTEC, Noordwijk, The Netherlands, pp. 1–162.

    Google Scholar 

  • Estes, J.E., Hajic E.J., Tinney L.R., (Author–Editors), 1980, Fundamentals of image analysis: analysis of visible and thermal infrared data, Chapter 24. In: Manual of Remote Sensing, 2nd ed. American Society of Photogrammetry, Falls Church, Virginia, pp. 987–1124.

    Google Scholar 

  • European Commission, 1993, CORINE Land Cover-Technical Guide. Directorate General Environment, Luxembourg, pp. 1–136.

    Google Scholar 

  • Gomarasca M.A., Strobelt S., 1996, Identification of Unknown Waste Sites Using MIVIS Hyperspectral Images. Proceedings 2nd International Airborne Remote Sensing Conference, San Francisco, 24–27 June 1996, Vol. III, pp. 335–342.

    Google Scholar 

  • Guyot G., 1989, Signatures spectrales des surfaces naturelles, Collection Teledetection satellitaire n. 5, Paradigme, p. 178.

    Google Scholar 

  • Hoffer R.M., 1978, Biological and physical considerations in applying computer-aided analysis techniques to remote sensor data. In: Remote Sensing: The Quantitative Approach, P.H. Swain and S.M. Davis (Eds.). McGraw-Hill Book Company, International Editions, New York pp. 227–289.

    Google Scholar 

  • Huete A.R., 1988, A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25: 295–309.

    Article  Google Scholar 

  • Hulbert E.O., 1945, Optics of distilled and natural water. Journal of the Optical Society of America, 35: 698–705.

    Article  Google Scholar 

  • Jacobsen K., 2007, 3D remote sensing. Status report. Proceedings of the 27th EARSeL Symposium, GeoInformation in Europe, M.A. Gomarasca (Ed.). Millpress, The Netherlands, pp. 591–599, ISBN 9789059660618.

    Google Scholar 

  • Kaufman Y.J., Tanré D., 1992, Atmospherically Resistant Vegetation Index (ARVI) for EOSMODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 261–270.

    Article  Google Scholar 

  • Kawamura M., Jayamana S., Tsujiko Y., 1996, Relation between social and environmental conditions in Colombo Sri Lanka and the Urban Index estimated by satellite remote sensing data, The International Archives of Photogrammetry and Remote Sensing, part B7, Vienna, 12–18 July, Vol. XXXI, pp. 321–326.

    Google Scholar 

  • Le Toan T., Ribbes F., Wang L.F., Floury N., et al., 1997, IEEE Transactions on Geoscience and Remote Sensing, 35(1): 41–55.

    Article  Google Scholar 

  • Kauth R.J., Thomas G.S., 1976, The Tasseled Cap: A graphic description of the spectra-temporal development of agricultural crops as seen by Landsat. Proceedings Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, IN, USA, pp. 4B41–4B51.

    Google Scholar 

  • Key C.H., Benson N.C., 1999. Measuring and remote sensing of burn severity. In: L.F. Neuenschwander and K.C. Ryan (Eds.). Proceedings Joint Fire Science Conference and Workshop, Vol. II, University of Idaho and International Association of Wildland Fire, Moscow, ID, p. 284.

    Google Scholar 

  • Key C.H., 2005, Remote sensing sensitivity to fire severity and fire recovery. In: E. Cuvieco, 5th international workshop on remote sensing and GIS applications to forest fire management: Fire effects assessment. Universidad de Zaragoza, Spain pp. 29–39.

    Google Scholar 

  • MacQueen J.B., 1967, Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, Berkeley, Vol. 1, pp. 281–297.

    Google Scholar 

  • Markham B.L., Barker J.L., 1986, Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. EOSAT Technical Notes.

    Google Scholar 

  • Martin M.P., 1998, Cartografia e inventario de incendios forestales en la Peninsula Iberica a partir de imagines NOAA AVHRR. Doctoral thesis, Universidad de Alcalà, Alcalà de Henares.

    Google Scholar 

  • Oosterom P.v., Zlatanova S., Penninga F., Fendel E. (Eds.), 2008, Advanced in 3D Geoinformation Systems. Springer, Berlin ISBN 978-3-540-72134-5.

    Google Scholar 

  • Orlov D.S., Bildebaeva R.M., Sadovnikov Yu N., 1976, Quantitative laws of reflection light by soils, sub-tropical soils of Western Georgia. Scientific Papers of High School Biology, 22: 127–132.

    Google Scholar 

  • Paine D.E., 1981, Aerial Photography and Image Interpretation for Resource Management. John Wiley and Sons, New York, p. 571.

    Google Scholar 

  • Parzen E., 1962, On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33: 1065–1076.

    Article  Google Scholar 

  • Pedrycz W., 1990, Fuzzy sets in pattern recognition: methodology and methods. Pattern Recognition, 23: 121–146.

    Article  Google Scholar 

  • Perry C.R., Lautenschlager L.F., 1984, Functional equivalence of spectral vegetation indices. Remote Sensing of Environment, 14: 169–182.

    Article  Google Scholar 

  • Pearson R.L., Miller L.D., 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado. In: Proceedings of the 8th International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, MI, pp. 1357–1381.

    Google Scholar 

  • Pinty B., Verstrate M., 1992, GEMI: a non-linear index to monitor global vegetation from satellites. Vegetation, 101: 15–20.

    Article  Google Scholar 

  • Qi J., Chehbouni A., Huete A.R., Kerr Y.H., Sorooshian S., 1994, A Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment, 48: 119–126.

    Article  Google Scholar 

  • Richardson A.J., Wiegand C.L., 1977, Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(2): 1541–1552.

    Google Scholar 

  • Ridler T.W., Calvard S., 1978, Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man and Cybernetics, 8: 630.

    Article  Google Scholar 

  • Rondeaux G., 1995, Vegetation monitoring by remote sensing: a review of biophysical indices. Photo Interpretation, 33(3): 197–212.

    Google Scholar 

  • Rondeaux G., Steven M., Baret F., 1996, Optimisation of Soil-Adjusted Vegetation Indices. Remote Sensing of Environment, 55(2): 95–107.

    Article  Google Scholar 

  • Rouse J.W., Haas R.H., Shell J.A., Deering D.W., Harlan J.C., 1974, Monitoring the vernal advancement of retrogradation of natural vegetation. Final Report, Type III, NASA/GSFC, Greenbelt, MD, p. 371.

    Google Scholar 

  • Rouse J.W., Haas R.H. Jr., Schell J.A., Deering D.W., 1974, Monitoring vegetation systems in the great plains with ERTS. NASA SP-351, 3rd ERTS-1 Symposium, Washington, DC, pp. 309–317.

    Google Scholar 

  • Slater P.N., Biggar S.F., Holm R.G. et al., 1987, Reflectance based methods and radiance based methods for the in-flight absolute calibration of multispectral sensors. Remote Sensing of Environment, 22: 11–37.

    Article  Google Scholar 

  • Slater P.N., Biggar S.F., Thome K.J., Gellman D.I., Spyak P.R., 1996, Vicarious radiometric calibrations of EOS sensors. Journal of Atmospheric and Oceanic Technology, 13: 349–359.

    Article  Google Scholar 

  • Smith R.C., Baker K.S., 1981, Optical properties of the clearest natural waters (200-800 nm). Applied Optics, 20: 177–184.

    Article  Google Scholar 

  • Stroppiana D., Brivio P.A., Zaffaroni P., Boschetti M., Mollicone D., Petrucci B., 2007, Burnt area mapping within the borders of the Italian National Parks using ASTER images. Proceedings of the 27th EARSeL Symposium, GeoInformation in Europe, M.A. Gomarasca (Ed.). Millpress, The Netherlands, pp. 211–217, ISBN 9789059660618.

    Google Scholar 

  • Swain P.H., Davis S.M., 1978, Remote Sensing: The Quantitative Approach, Mc Graw-Hill Book Company, Paris, New York.

    Google Scholar 

  • Thome K., Markham B., Barker J., Slater P., Biggar S., 1997, Radiometric calibration of Landsat. Photogrammetric Engineering and Remote Sensing, 63(7): 853–858.

    Google Scholar 

  • van Genderen J.L., Lock B.F., Vass P.A., 1978, Remote sensing: statistical testing of thematic map accuracy. Remote Sensing of Environment, 7: 3–14.

    Article  Google Scholar 

  • Villa P., 2007, Imperviousness Indexes Performance Evaluation for Mapping Urban Areas Using Remote Sensing Data, Urban Remote Sensing Joint Event, 2007, Paris, 11–13 April, pp. 1–6, ISBN: 1-4244-0712-5.

    Google Scholar 

  • Villa P., 2008, Urban Areas Changes Mapping Using Mid-resolution Satellite Data and Spectral Indexes: a case study in Milan, Italy, 1984–2003. IEEE Transactions on Geoscience and Remote Sensing – Special Issue on Remote Sensing of Human Settlements.

    Google Scholar 

  • Zadeh L.A., 1973, Fuzzy Pattern Recognition. Academic Press, London.

    Google Scholar 

  • Zilioli E., Brivio P.A., Gomarasca M.A., 1994, A correlation between the optical properties of Lacustrine waters and some indicators of eutrophication. The Science of the Total Environment. Elsevier Ed, Amsterdam, The Netherlands, Vol. 158, pp. 127–133.

    Google Scholar 

  • Zilioli E., Bianchi R., Giardino C., Gomarasca M.A., 1996, Potential of Remote Sensing for Monitoring Interactions Between Rivers and Urban Settlements, Metropolitan Areas and Rivers, Roma, 27–31 May 1996, Vol. 1, pp. 119–127.

    Google Scholar 

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Authors and Affiliations

  1. National Research Council of Italy, Institute for the Electromagnetic Sensing of the Environment, Via Bassini, 15, 20133, Milano, Italy

    Mario A. Gomarasca

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  1. Mario A. Gomarasca
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Correspondence to Mario A. Gomarasca .

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Gomarasca, M.A. (2009). Digital Image Processing. In: Basics of Geomatics. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9014-1_8

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