Computational Intelligence and its Application in Remote Sensing

  • Habtom Ressom
  • Richard L. Miller
  • Padma Natarajan
  • Wayne H. Slade
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 7)


Root Mean Square Error Membership Function Hide Layer Remote Sensing Fuzzy Rule 
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  1. Amari, S. 1995. Learning and statistical inference. In: The Handbook of Brain Theory and Neural Networks. Arbib, M.A. (Ed.). MIT Press, Cambridge, MA, 522-526.Google Scholar
  2. Arenz R.F. Jr., W.M. Lewis Jr., J.F. Saunders III. 1996. Determination of chlorophyll and dissolved organic carbon from reflectance data for Colorado reservoirs. International Journal of Remote Sensing, 17(8): 1547-1566.CrossRefGoogle Scholar
  3. Arrigo, K.P., and C.W. Brown. 1996. Impact of chromophoric dissolved organic matter on UV inhibition of primary productivity in the sea. Marine Ecology Progress Series, 140:207-216.CrossRefGoogle Scholar
  4. Baruah, P.J., K. Oki, and H. Nishimura. 2000. A neural network model for estimating surface chlorophyll and sediment content at the Lake Kasumi Gaura of Japan, Proceedings, Asian Conference on Remote Sensing, 418-424.Google Scholar
  5. Behrenfeld, M.J. and P.G. Falkowski. 1997. Photosynthetic rates derived from satellite based chlorophyll concentration, Limnology and Oceanography, 42(1):1-20.Google Scholar
  6. Bhargava, D.S., and D.W. Mariam. 1992. Cumulative effects of salinity and sediment concentration of reflectance measurements. International Journal of Remote Sensing, 13(11):2151-2159.CrossRefGoogle Scholar
  7. Bricaud, A., A. Morel and L. Prieur. 1981. Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domain. Limnology and Oceanography, 26:43-53.Google Scholar
  8. Buckton, D., E. O’Monogan, and S. Danaher. 1999. The use of neural networks for the estimation of oceanic constituents based on the MERIS instrument. International Journal of Remote Sensing, 20(9):1841-1851.CrossRefGoogle Scholar
  9. Bezdek, J.C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York.Google Scholar
  10. Carder, K.L., F.R. Chen, Z.P. Lee, S. Hawes, and D. Kamykowski. 1999. Semianalytic MODIS algorithms for chlorophyll-a and absorption with bio-optical domains based on nitrate-depletion temperatures. Journal of Geophysical Research, 104(C3):5403-5421.Google Scholar
  11. Carder, K.L., S.K. Hawes, K.A. Baker, R.C. Smith, R.G. Steward, and B.G. Mitchell. 1991. Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products. Journal of Geophysical Research – Oceans, 96(C11):20599-20611.Google Scholar
  12. Cipollini, P., G. Corsini, M. Diani, and R. Grasso. 2001. Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks. IEEE Transactions on Geoscience and Remote Sensing, 39(7):1508-1524.CrossRefGoogle Scholar
  13. Corsini, G., M. Diani, R. Grasso, B. Lazzerini, F. Marcelloni, M. Cococcioni. 2002. A fuzzy model for the retrieval of the sea water optically active constituents concentration from MERIS data. IEEE International Geoscience and Remote Sensing Symposium, vol. 1, 98-100.CrossRefGoogle Scholar
  14. Doerffer, R. and J. Fischer. 1994. Concentrations of chlorophyll suspended matter and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods. Journal of Geophysical Research, 99(C4):7457-7466.Google Scholar
  15. Doerffer, R. and H. Schiller. 1998. Algorithm Theoretical Basis Document (ATBD 2.12): Pigment index, sediment and gelbstoff retrieval from directional water leaving radiance reflectances using inverse modelling technique. ESA Doc. No. PO-TN-MEL-GS-0005, 12-1 - 12-60.Google Scholar
  16. Engelbrecht, A.P. 2002. Computational Intelligence: An Introduction. John Wiley and Sons, Inc., England.Google Scholar
  17. Esaias, W.E., M.R. Abbott, I. Barton, O.B. Brown, J.W. Campbell, K.L. Carder, D.K. Clark, R.H. Evans, F.E. Hoge, H.R. Gordon, W.M. Balch, R. Letelier, and P.J. Minnett. 1998. An Overview of MODIS Capabilities for Ocean Science Observations. IEEE Transactions on Geoscience and Remote Sensing, 36(4):1250-1265.CrossRefGoogle Scholar
  18. Froidefond, J.M., P. Castaing, J.M. Jouanneau, R. Prudhomme and A. Dinet. 1993. Method for the quantification of suspended sediments from AVHRR NOAA-11 satellite data. International Journal of Remote Sensing, 14(5):885-894.CrossRefGoogle Scholar
  19. Garver, S.A. and D.A. Siegel. 1997. Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation. 1. Time series from the Sargasso Sea. Journal of Geophysical. Research, 102 (C8): 18607-18625.Google Scholar
  20. George D.G. 1997. The airborne remote sensing of phytoplankton chlorophyll in the lakes and tarns of the English Lake District. International Journal of Remote Sensing, 18(9):1961-1975.CrossRefGoogle Scholar
  21. Goldberg, D.E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass.Google Scholar
  22. Gordon, H.R., and A.Y. Morel. 1983. Remote assessment of ocean color for interpretation of satellite visible imagery: a review. In: Lecture Notes on Coastal and Estuarine Studies, vol. 4, M. Bowman (ed.). Springer-Verlag, New York. 1-114.Google Scholar
  23. Gordon, H.R., O.B. Brown, R.H. Evans, J.W. Brown, R.C. Smith, K.S. Baker, D.K. Clark. 1988. A semianalytic radiance model of ocean color. Journal of Geophysical Research, 93:10909-10924.CrossRefGoogle Scholar
  24. Haykin, S. 1999. Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, Upper Saddle River, NJ.Google Scholar
  25. Hedges J.I., G. Eglinton, P.G. Hatcher et al. 2000. The moleculary-uncharacterised component of nonliving organic matter in natural environments. Organic Geochemistry, 31:945-958.CrossRefGoogle Scholar
  26. Hoge, F.E., R.N. Swift, and J.K. Yungel. 1995. Oceanic radiance model development and validation: application of airborne active-passive ocean color spectral measurements. Applied Optics, 34:3468-3476.CrossRefGoogle Scholar
  27. Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. Howard, K.L. and J.A. Yoder. 1997. Contribution of the subtropical oceans to global primary productivity. In: Proceedings of COSPAR Colloquium, Space Remote Sensing of Subtropical Oceans (C.T. Liu, ed), COSPAR Colloquia Series vol. 8, Pergamon. pg 157-168.Google Scholar
  28. Hu, C., A.L. Odriozola, J.P. Akl, F.E. Muller-Karger, R. Varela, Y. Astor, P. Swarzenski, and J.M. Froidefond. 2002. Remote sensing algorithms for river plumes: A comparison, ASLO2002, Victoria, British Columbia, Canada, 10-14.Google Scholar
  29. Keiner L.E. and C.W. Brown. 1999. Estimating oceanic chlorophyll concentrations with neural networks, International Journal of Remote Sensing, 20(1):189-194.CrossRefGoogle Scholar
  30. Keiner, L.E. and X. Yan. 1998. A neural network model for estimating sea surface chlorophyll and sediments from Thematic Mapper imagery. Remote Sensing of Environment, 66:153-165.CrossRefGoogle Scholar
  31. Khan, M.A., Y.H. Fadlallah, and K.G. Al-Hinai. 1992. Thematic mapping of subtidal coastal habitats in the western Arabian Gulf using Landsat TM data - Abu Ali Bay, Saudi Arabia. International Journal of Remote Sensing, 13(4):605-614.CrossRefGoogle Scholar
  32. Kirk, J.T.O. 1994. Light and Photosynthesis in Aquatic Ecosystems, 2nd ed., Cambridge University Press: Cambridge.CrossRefGoogle Scholar
  33. Kishino, M., A. Tanaka, T. Oishi, R. Doerffer, H. Schiller. 2001. Temporal and spatial variability of chlorophyll a, suspended solids, and yellow substance in the Yellow Sea and East China Sea using ocean color sensor, Proc. SPIE, vol. 4154, pg 179-187.Google Scholar
  34. Kohonen, T. 2001. Self-Organizing Maps. 3rd Edition. Springer-Verlag, Berlin, Heidelberg, New York.Google Scholar
  35. Lee, Z., K.L. Carder, C.D. Mobley, R.G. Steward, and J.S. Patch. 1999. Hyperspectral remote sensing for shallow waters: Deriving bottom depths and water properties by optimization. Applied Optics, 38:3831- 3843.CrossRefGoogle Scholar
  36. Lin, C.T. 1994. Neural fuzzy control systems structure and parameter learning, World Scientific Co. Ltd. Maass, W. 1995. Vapnik-Chervonenkis dimension of neural networks. In: The Handbook of Brain Theory and Neural Networks.Arbib, M.A. (Ed.). MIT Press, Cambridge, MA, 522-526.Google Scholar
  37. McCarthy M., T. Pratum, J. Hedges, R. Benner. 1997. Chemical composition of dissolved nitrogen in the ocean. Nature, 390:150-153.CrossRefGoogle Scholar
  38. Medsker, L.R. 1995. Hybrid intelligent systems. Kluwer Academic Publishers, Boston, MA, USA.Google Scholar
  39. Mitchell, M.. 1998. An Introduction to Genetic Algorithms (Complex Adaptive Systems), MIT Press, Cambridge, MA.Google Scholar
  40. Moody, J. 1992. The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems. In: Advances in Neural Information Processing Systems. Moody, J., S. J. Hanson, and R.P. Lippmann (Eds.). Morgan Kaufmann, San Mateo, CA, 847-854.Google Scholar
  41. Moore, T.S., J.W. Campbell, and H. Feng. 2001. A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms. IEEE Transactions on Geoscience and Remote Sensing, 39(8): 1764-1776.CrossRefGoogle Scholar
  42. Morel, A. 1988. Optical modeling of the upper ocean in relation to its biogenous matter content (case I waters). Journal Geophysical Research, 93:10749-10768.CrossRefGoogle Scholar
  43. Musavi, M.T., R.L. Miller, H. Ressom, and P. Natarajan. 2001. Neural network-based estimation of chlorophyll-a concentration in coastal waters. In Proceedings of SPIE, 4488, pg 176-183.Google Scholar
  44. Neale P.J., J.J. Cullen, and R.F. Davis. 1998. Inhibition of marine photosynthesis by ultraviolet radiation: Variable sensitivity of phytoplankton in the Weddell-Scotia Sea during the austral spring, Limnology and Oceanography, 43(3):433-448.CrossRefGoogle Scholar
  45. O'Reilly, J.E., S. Maritorena, D.A. Siegel, M.C. O’Brien, D. Toole, B.G. Mitchell, M. Kahru, F.P. Chavez, P. Strutton, G.F. Cota, S.B. Hooker, C.R. McClain, K.L. Carder, F. Müller-Karger, L. Harding, A. Magnuson, D. Phinney, G.F. Moore, J. Aiken, K.R. Arrigo, R. Letelier, M. Culver. 2000. Ocean color chlorophyll a algorithms for SeaWiFS, OC2, OC4: version 4. NASA-TM-2000-206892, 11:9-23.Google Scholar
  46. O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, and C. O McClain. 1998. Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research, 103 (C11):24,937-24,953.Google Scholar
  47. Orr, M., J. Hallam, K. Takezawa, A. Murray, S. Nimomiya, M. Oide, and T. Leonard. 2000. Combining regression trees and radial basis function networks. International Journal of Neural Systems, 10(6):453- 465.Google Scholar
  48. Ressom, H., M.T. Musavi, P. Natarajan. 2001. Neural network-based estimation of phytoplankton primary production, Proceedings of SPIE, vol. 4488, pg 213-220.Google Scholar
  49. Ritchie, J.C., and C.M. Cooper. 1988. Comparison of measured suspended sediment concentrations with suspended sediment concentrations estimated from Landsat MSS data, International Journal of Remote Sensing, 9(3):379-387.CrossRefGoogle Scholar
  50. Roesler, C.S., and M.J. Perry. 1995. In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance. Journal of Geophysical Research, 100(C7):13279 – 13294.Google Scholar
  51. Rumelhart, D.E. and J.L. McClelland (Eds.). 1986. Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1: Foundations, MIT press.Google Scholar
  52. Salomonson, V.V., W.L. Barnes, P.W. Maymon, H.E. Montgomery, and H. Ostrow. 1989. MODIS: advanced facility instrument for studies of the Earth as a system. IEEE Transactions of Geoscience and Remote Sensing, 27: 145-152.CrossRefGoogle Scholar
  53. Sathyendranath S., L. Priuer, A. Morel. 1989. A three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters. International Journal of Remote Sensing 10 (8):1373-1394.CrossRefGoogle Scholar
  54. Scardi, M. 1996. Artificial neural networks as empirical models of phytoplankton production, Marine Ecology Progress Series, 139:289-299.CrossRefGoogle Scholar
  55. Scardi, M. 2001. Advances in neural network modeling of phytoplankton primary production. Ecological Modelling, 146(1-3):33-45.CrossRefGoogle Scholar
  56. Schiller, H. and R. Doerffer. 1999. Neural network for emulation of an inverse model – operational derivation of Case II water properties from MERIS data. International Journal of Remote Sensing, 20(9):1735-1746.CrossRefGoogle Scholar
  57. Schwarz, J.N., P. Kowalczuk, S. Kaczmarek, G. F. Cota, B. G. Mitchell, M. Kahru, F. P. Chavez, A. Cunningham, D. McKee, P. Gege, M. Kishino, D. A. Phiney, R. Raine. 2002. Two models for absorption by colored dissolved organic matter (CDOM). Oceanologia, 44(2):209-241.Google Scholar
  58. Siegel H., M. Gerth, M. Beckert. 1994. The variation of optical properties in the Baltic sea and algorithms for the application of remote sensing. SPIE vol. 2258: 894-905.CrossRefGoogle Scholar
  59. Siegel, D.A., A.F. Michaels. 1996. Quantification of non-algal attenuation in the Sargasso Sea: implications for biogeochemistry and remote sensing. Deep-Sea Research II, 43:321-345.CrossRefGoogle Scholar
  60. Slade, W.H., R.L. Miller, H. Ressom, and P. Natarajan. 2004. Neural network retrieval of phytoplankton abundance from remotely-sensed ocean radiance. In Proceedings of 2nd IASTED International Conference on Neural Networks and Computational Intelligence, Grindelwald, Switzerland.Google Scholar
  61. Tassan, S. 1993. An improved in-water algorithm for the determination of chlorophyll and suspended sediment concentration from Thematic Mapper data in coastal waters. International Journal of Remote Sensing, 14 (6):1221-1229.CrossRefGoogle Scholar
  62. Topliss, B.J., C.L. Almos, and P.R. Hill. 1990. Algorithms for remote sensing of high concentration inorganic suspended sediment. International Journal of Remote Sensing, 11(6):947-966.CrossRefGoogle Scholar
  63. Tso, B. and P.M. Mather. 2001. Classification Methods for Remotely Sensed Data, Published by Taylor and Francis, London.Google Scholar
  64. Wernarnd, M.R., S.J. Shimwell, and J.C. Munck. 1997. A simple method of full spectrum reconstruction by a five band approach for ocean colour applications. International Journal of Remote Sensing, 18(9):1977- 1986.CrossRefGoogle Scholar
  65. Wilkinson, G.G. 1996. A review of current issues in the integration of GIS and remote sensing data. International Journal of Geographical Information Systems, 10(1): 85-101.Google Scholar
  66. Yen, J. and R. Langari. 1999. Fuzzy Logic: Intelligence, Control, and Information, Prentice Hall, Upper Saddle River, NJ.Google Scholar
  67. Zadeh, L.A. 1994. Fuzzy Logic, Neural Networks and Soft Computing. Communications of the ACM, 37(3): 77-84.CrossRefGoogle Scholar
  68. Zhan, H., Z. Lee, P. Shi, C. Chen, and K.L. Carder. 2003. Retrieval of water optical properties for optically deep waters using genetic algorithms. IEEE Transactions of Geoscience and Remote Sensing, 41(5): 1123-1128.CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Habtom Ressom
    • 1
  • Richard L. Miller
    • 2
  • Padma Natarajan
    • 3
  • Wayne H. Slade
    • 4
  1. 1.Lombardi Comprehensive Cancer Center, Biostatistics Shared Resource, Department of OncologyGeorgetown University Medical CenterWashingtonUSA
  2. 2.National Aeronautics and Space AdministrationEarth Science Applications Directorate, Stennis Space CenterUSA
  3. 3.Department of Electrical and Computer EngineeringUniversity of MaineOronoUSA
  4. 4.Department of Electrical and Computer EngineeringUniversity of MaineOronoUSA

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