Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements

International Journal of Remote Sensing

Volumn 19, Issue 14, 1998, Pages 2639-2663

  Kimes, D S ^a   Nelson, R F ^a   Manry, M T ^b   Fung, A K ^b  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b University of Texas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; FUNCTIONS; IMAGE ANALYSIS; LEARNING SYSTEMS; NEURAL NETWORKS; NONLINEAR EQUATIONS; OPTICAL SENSORS; RADAR IMAGING; RADAR MEASUREMENT; SENSOR DATA FUSION; VEGETATION;

NONLINEAR ACTIVATION FUNCTIONS;

REMOTE SENSING;

EXTRACTION METHOD; REMOTE SENSING; VEGETATION;

EID: 0032552629     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/014311698214433     Document Type: Review

References (116)

1. AbueLgasim, A. A., Gopal, S., and StrahLer, A. H 1998, Forward and inverse modeling of canopy directional reflectance using a neural network.International Journal of Remote Sensing19, 453-471.
2. Akaike, H., 1974, A new look at the statistical model identification.IEEE Transactions on Automatic Control, 19, 716-723.
3. Anderson, J. A., and RosenfeLd, E., (editors), 1988, Neurocomputing: Foundations of Research,(Cambridge, MA: MIT Press).
4. Apollo, S. J., Manry, M. T., ALLen, L. S., and Lyle, W. D., 1992, Optimality of transforms for parameter estimation.Conference Record of the Twenty-Sixth Annual Asilomar Conference on Signals, Systems, and Computers, October 1992(Piscataway, NJ: IEEE), vol. 1 pp. 294-298.
5. Asrar, G., and Dozier, J., 1994, EOS—Science Strategy for the Earth Observing System,(Woodbury, New York: AIP Press).
6. Asrar, G., and Greenstone, R., (editors), 1995, MTPE EOS Reference Handbook. EOS Project Science Office, NASA/GSfC, Greenbelt, Maryland, USA.
7. Atkinson, P. M., and Tatnall, A. R. L., 1997, Neural networks in remote sensing.International Journal of Remote Sensing, 18, 699-709.
8. Attema, E. P. W., and ULaby, F. T., 1978, Vegetation modeled as a water cloud.Radio Science, 13, 357-364.
9. Baret, F., CLevers, J. G. P. W., and Steven, M. D., 1995, The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches.Remote Sensing of Environment, 54, 141-151.
10. Butini, F., CappeLLini, V., and Fini, S., 1992, Remote sensing data fusion on intelligent terminals.European Transactions on Telecommunications and Related Technologies, 3, 555-563.
11. Chatterjee, S., and Price, B., 1991, Regression Analysis by Example(New York: Wiley).
12. Chen, M. S., and Manry, M. T., 1990, Back-propagation representation theorem using power series.Proceedings of International Joint Conference on Neural Networks (IJCNN) 90, San Diego, California 17-21 June 1990(Piscataway, NJ: IEEE), pp. 643-648.
13. Chen, Z., Tsang, L., Kuga, Y., and Chan, C., 1993, Active polarimetric microwave remote sensing of corn and multiparametric retrieval of corn parameters with an artificial neural network.Microwave and Optical Technology Letters, 6, 611-615.
14. Chuah, H. T., 1993, An artificial neural network for inversion of vegetation parameters from radar backscatter coefficients.Journal of Electromagnetic Waves and Applications, 7, 1075-1092.
15. Chuah, H. T., and Tan, H. S., 1989, A Monte Carlo method for radar backscatter from a half-space random medium.IEEE Transactions on Geoscience and Remote Sensing, 27, 86-93.
16. Conins, J. B., and Woodcock, C. E., 1995, Change detection using the Gramm-Schmidt transformation applied to mapping forest mortality.Remote Sensing of Environment, 50, 267-279.
17. Cybenko, G., 1989, Approximations by superpositions of a sigmoidal function.Mathematical Control, Signals, and Systems, 2, 303-314.
18. Dawson, M. S., 1994, Applications of electromagnetic scattering models to parameter retrieval and classification. InMicrowave Scattering and Emission Models and Their Applications, chapter 12, edited by A. K. Fung. (Norwood, MA: Artech House).
19. Dawson, M. S., Fung, A. K., and Manry, M. T., 1993, Surface parameter retrieval using fast learning neural networks.Remote Sensing Reviews, 7, 1 18.
20. Dawson, M. S., Fung, A. K., and Manry, M. T., 1997, A robust statistical-based estimator for soil moisture retrieval from radar measurements.IEEE Transactions on Geoscience and Remote Sensing, 35, 57-67.
21. Dobson, M. C., Ulaby, F. T., Le Toan, T., Beaudoin, A., Kasischke, E. S., and Christensen, N. L. Jr, 1992, Dependence of radar backscatter on coniferous forest biomass.IEEE Transactions on Geoscience and Remote Sensing, 30, 412-415.
22. Dobson, M. C., Ulaby, F. T., Pierce, L. E., Sharik, T. L., Bergen, K. M., Kenndorfer, J., Kendra, J. R., Li, E., Lin, Y. C., Nashashibi, A., Sarabandi, K., and Siqueira, P., 1995, Estimation of forest biophysical characteristics in Northern Michigan with SIR-C/XSAR.IEEE Transactions on Geoscience and Remote Sensing, 33, 877-895.
23. Draper, N. R., and Smith, H., 1966, Applied Regression Analysis(New York: Wiley).
24. Fahlmann, S., and Lebiere, C., 1988, The cascade-correlation learning architecture. InAdvances in Neural Information Processing Systems, vol. 2, edited by D. Touretzky (Aan Mateo, CA: Morgan Kaufmann), pp. 524-532.
25. Fogel, D. B., 1991, An information criterion for optimal neural network selection.IEEE Transactions on Neural Networks, 2, 490-497.
26. Fourty, T., and Baret, F., 1997, Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: a simulation study.Remote Sensing of Environment, 61, 34-45.
27. Fu, L., 1994, Neural Networks in Computer Intelligence(New York: McGraw-Hill).
28. Fung, A. K., 1994, Microwave Scattering and Emission Models and Their Applications(Norwood, MA: Artech House).
29. G ao, B. C., 1996, NDWI—a normalized difference water index for remote-sensing of vegetation liquid water from space.Remote Sensing of Environment, 58, 257-266.
30. Girosi, F., and Poggio, T., 1989, Representation properties of networks: Kolmogorov’s theorem is irrelevant.Neural Computation, 1, 465-469.
31. Goel, N., 1987, Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data.Remote Sensing Reviews, 3, 1-212.
32. Gopal, S., and Woodcock, C., 1996, Remote sensing of forest change using artificial neural networks.IEEE Transactions on Geoscience and Remote Sensing, 34, 398-404.
33. GopaLakrishnan, A., Jiang, X., Chen, M.-S., and Manry, M. T., 1994, Constructive proof of efficient pattern storage in the multilayer perceptron.Conference Record of the T wenty-Seventh Annual Asilomar Conference on Signals, Systems, and Computers, November 1994 (Piscataway, NJ: IEEE), vol. 1, pp. 306-390.
34. Govaerts, Y., and Verstraete, M. M., 1994, Evaluation of the capability of BRDF models to retrieve structural information on the observed target as described by a threedimensional ray tracing code.Proceedings of the Society of Photo-Optical Instrumentation Engineers, 2314, 9-20.
35. Goward, S. N., Markham, B., Dye, D. G., DuLaney, W., and Yang, J., 1991, Normalized difference vegetation index measurements from the advanced very high resolution radiometer.Remote Sensing of Environment, 35, 257-277.
36. Gutman, G. G., 1991, Vegetation indices from AVHRR: an update and future prospects.Remote Sensing of Environment, 35, 121-136.
37. Hall, F. G., Townshend, J. R., and Engman, E. T., 1995, Status of remote sensing algorithms for estimation of land surface state parameters.Remote Sensing of Environment, 51, 138-156.
38. Hame, T., and SaLLi, A., 1994, Estimation of forest stand characteristics using Landsat TM, NOAA AVHRR, ERS-1 SAR, and other image data.Proceedings of the Society of Photo-Optical Instrumentation Engineers, 2314, 233-243.
39. Hartman, E. J., KeeLer, J. D., and KowaLski, J. M., 1990, Layered neural networks with Gaussian hidden units as universal approximations.Neural Computation, 2, 210-215.
40. Haykin, S., 1994, Neural Networks, A Comprehensive Foundation(New York: Macmillan).
41. Hecht-NieLsen, R., 1989, Theory of backpropagation neural networks.Proceedings of International Joint Conference on Neural Networks (IJCNN)’89, Washington D.C., 18-22 June 1989(Piscataway, NJ: IEEE), vol. 1, pp. 593-605.
42. Hewitson, B. C., and Crane, R. F., 1994, Looks and uses. InNeural Nets: Applications in Geography, edited by B. C. Hewitson and R. C. Crane (Dordrecht: Kluwer).
43. Hornik, K., Stinchcombe, M., and White, H., 1990, Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks.Neural Networks, 3, 551-560.
44. Hrycej, T., 1990, A modular architecture for efficient learning.Proceedings of International Joint Conference on Neural Networks (IJCNN)’90, San Diego, California 8-12 July 1990(Piscataway, NJ: IEEE), vol. I, pp. 557-562.
45. Huete, A. R., 1988, A soil-adjusted vegetation index (SAVI).Remote Sensing of Environment, 25, 295-309.
46. Huete, A. R., Jackson, R. D., and Post, D. F., 1985, Spectral response of a plant canopy with different soil backgrounds.Remote Sensing of Environment, 17, 37-53.
47. Huete, A. R., Justice, C., and Liu, H., 1994, Development of vegetation and soil indices for MODIS-EOS.Remote Sensing of Environment, 49, 224-234.
48. Jacobs, R. A., and Jordan, M. I., 1993, Learning piecewise control strategies in a modular neural network architecture.IEEE Transactions on Systems, Man, and Cybernetics, 23, 337-345.
49. Jacquemoud, S., 1993, Inversion of the PROSPECT + SAIL canopy reflectance model from AVIRIS equivalent spectra—theoretical study.Remote Sensing of Environment, 44, 281-292.
50. Jacquemoud, S., and Baret, F., 1990, PROSPECT: a model of leaf optical properties spectra.Remote Sensing of Environment, 34, 75-91.
51. Jakubauskas, M. E., 1996, Thematic mapper characterization of Lodgepole Pine seral stages in Yellowstone National Park, USA.Remote Sensing of Environment, 56, 118-132.
52. Jiang, X., Chen, M., Manry, M. T., Dawson, M. S., and Fung, A. K., 1994, Analysis and optimization of neural networks for remote sensing.Remote Sensing Reviews, 9, 97-114.
53. Kasischke, E. S., Bourgeau-Chavez, L. L., Christensen, N. L. Jr. and Dobson, M. C., 1991, The relationship between above ground biomass and radar backscatter observed on airborne SAR imagery.Proc. Third AIRSAR Workshop, NASA, JPL, Pasadena, California, May 1991, edited by J. J. van Zyl, pp. 11-21.
54. Kasischke, E. S., Christensen, N. L. Jr and Bourgeau-Chavez, L. L., 1995, Correlating radar backscatter with components of biomass in Loblolly pine forests.IEEE Transactions on Geoscience and Remote Sensing, 33, 643-659.
55. Karam, M. A., Fung, A. K., Lang, R. H., and Chauhan, N. S., 1992, A microwave scattering model for layered vegetation.IEEE Transactions on Geoscience and Remote Sensing, 30, 767-784.
56. Kaufman, K. J., and Tanre, D., 1992, Atmospherically resistant vegetation index for EOS-MODIS.IEEE Transactions on Geoscience and Remote Sensing, 30, 261-270.
57. Kim, K. K., and Manry, M. T., 1995, A complexity algorithm for estimating the size of the multilayer perceptron.Conference Record of the Twenty-Ninth Annual Asilomar Conference on Signals, Systems, and Computers 29 October-1 November 1995(Piscataway, NJ: IEEE), vol. 2, pp. 899-903.
58. Kimes, D. S., HoLben, B. N., Nickeson, J. E., and McKee, A., 1996, Extracting forest age in a Pacific Northwest forest from thematic mapper and topographic data.Remote Sensing of Environment, 56, 133-140.
59. Kimes, D. S., Ranson, K. J., and Sun, G., 1997, Inversion of a forest backscatter model using neural networks.International Journal of Remote Sensing, 18, 2181-2199.
60. Koza, J., 1993, Genetic Programming(Cambridge, MA: MIT Press).
61. Kuusk, A., 1991, The hot spot effect in plant canopy reflectance. InPhoto-Vegetation Interaction. Applications in Optical Remote Sensing and Plant Ecology, edited by R. B. Myneni and J. Ross (Berlin: Springer-Verlag), pp. 139-159.
62. Kuusk, A., 1994, Multispectral canopy reflectance model.Remote Sensing of Environment, 50, 75-82.
63. Leprieur, C., Verstraete, M. M., Pinty, B., and Chehbouni, A., 1994, NOAA/AVHRR vegetation indices: suitability for monitoring fractional vegetation cover of the terrestrial biosphere.Physical measurements and signatures in Remote Sensing, Sixth Symposium ofInternational Society ofPhotogrammetric and Remote Sensing, Val d'Isere, France, 17-21 January 1994, pp. 1103-1110.
64. Le Toan, T., Beaudoin, A., Riom, J., and Guyon, D., 1992, Relating forest biomass to SAR data.IEEE Transactions on Geoscience and Remote Sensing, 30, 403-411.
65. Li, X., and StrahLer, A. H., 1992, Geometric optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effects of crown chape and mutual shadowing.IEEE Transactions on Geoscience and Remote Sensing, 30, 276-292.
66. Liang, W., Manry, M. T., Yu, Q., Apollo, S. J., Dawson, M. S., and Fung, A. K., 1994, Bounding the Performance of neural network estimators, given only a set of training data.Conference Record of the T wenty-Eighth Annual Asilomar Conference on Signals, Systems, and Computers 31 October-2 November 1994(Piscataway, NJ: IEEE), vol. 2, pp. 912-916.
67. Lippman, R. P., 1987, An introduction to computing with neural nets.IEEE ASSP Magazine, April, 4-22.
68. Manry, M. T., 1995, A self-organizing system for the development ofneural network parameter estimators.IEEE Workshop on Neural Networks for Signal Processing (NNSP’95), Cambridge, Massachusetts 31 August-2 September 1995, pp. 105-114.
69. Manry, M. T., Apollo, S. J., and Yu, Q., 1996a, Minimum mean square estimation and neural networks.Neurocomputing, 13, 59-74.
70. M anry, M. T., D awson, M. S., Fung, A. K., A pollo, S. J., A LLen, L. S., LyLe, W. D., and Gong, W., 1994, Fast training of neural networks for remote sensing.Remote Sensing Reviews, 9, 77-96.
71. Manry, M. T., Hsieh, C.-H., Apollo, S. J., Dawson, M. S., and Fung, A. K., 1996b, Cramer-Rao maximuma posterioribounds for non-Gaussian signals and parameters.International Journal of Intelligent Control and Systems, 1, 381-391.
72. Manry, M. T., ShouLts, R., and Naccarino, J., 1996c, An automated system for developing neural network short term load forecasters.Proceedings of the 58th American Power Conference, Chicago, Illinois, April 9-11, 1996(Chicago, IL: Illinois Institute of Technology), vol. 1, pp. 237-241.
73. M oghaddam, M., 1994, Retrieval of forest canopy parameters for OTTER.Proceedings of the Society of Photo-Optical Instrumentation Engineers, 2314, 549-558.
74. McDonaLd, K. C., and ULaby, F. T., 1993, Radiative transfer modeling of discontinuous tree canopies at microwave frequencies.International Journal of Remote Sensing, 14, 2097-2128.
75. Murata, N., Yoshizawa, S., and Amari, S., 1994, Network information criterion-determining the number of hidden units for an artificial neural network model.IEEE Transactions on Neural Networks, 5, 865-872.
76. Myneni, R. B., Hall, F. G., Sellars, P. J., and Marshak, A. L., 1995, The interpretation of spectral vegetation indices.IEEE T ransactions on Geoscience and Remote Sensing, 33, 481-486.
77. Myneni, R. B., and Ross, J., 1991, Photon-Vegetation Interactions(New York: SpringerVerlag).
78. M yneni, R. B., R oss, J., and A srar, G., 1989, A review on the theory of photon transport in leaf canopies.Agricultural Forest Meteorology, 45, 1-153.
79. NowLan, S. J., 1990, Maximum likelihood competitive learning. InAdvances in Neural Information Processing Systems, vol. 2, edited by D. S. Touretzky, pp. 574-582.
80. Pao, Y.-H., 1989, Adaptive Pattern Recognition and Neural Networks(New York: Addison-Wesley).
81. Pierce, L. E., Sarabandi, K., and ULaby, F. T., 1994, Application of an artificial neural network in canopy scattering inversion.International Journal of Remote Sensing, 15, 3263-3270.
82. Pinty, B., and Verstraete, M. M., 1991, Extracting information on surface properties from bidirectional reflectance measurements.Journal of Geophysical Research, 96, 2865-2874.
83. Pinty, B., and Verstraete, M. M., 1992, GEMI: A non linear index to monitor global vegetation from satellites.Vegetation, 101, 15-20.
84. Pinty, B., Verstraete, M. M., and Dickinson, R. E., 1990, A physical model of the bidirectional reflectance of vegetation canopies. 2. Inversion and validation.Journal of Geophysical Research, 95, 11767-11775.
85. PoLatin, P. F., Sarabandi, K., and ULaby, F. T., 1994, An iterative inversion algorithm with application to the polarimetric radar response of vegetation canopies.IEEE Transactions on Geoscience and Remote Sensing, 32, 62-71.
86. Prevot, L., and Schmugge, T., 1994, Combined use of theoretical and semi-empirical models of radar backscatter to estimate characteristics of canopies.Physical Measurements and signatures in Remote Sensing, Sixth Symposium of International Society of Photogrammetric and Remote Sensing, Val d’Isere, France, 17-21 January 1994, pp. 415-430.
87. Privette, J. L., Myneni, R. B., and Emery, W. J., 1996, Optimal sampling conditions for estimating grassland parameters via reflectance model inversions.IEEE Transactions on Geoscience and Remote Sensing, 34, 272-284.
88. Privette, J. L., Myneni, R. B., Tucker, C. J., and Emery, W. J., 1994, Invertibility of a 1-D discrete ordinates canopy reflectance model.Remote Sensing of Environment, 48, 89-105.
89. Qi, J., and Kerr, Y. H., 1994a, On current compositing algorithms.Physical Measurements and Signatures in Remote Sensing, Sixth Symposium of International Society of Photogramme tric and Remote Sensing, Val d’Isere, France, 17-21 January 1994, pp. 135-142.
90. Qi, J., Kerr, Y. H., and Chehbouni, A., 1994 b, External factor consideration in vegetation index development.Physical Measurements and Signatures in Remote Sensing, Sixth Symposium ofInternational Society ofPhotogrammetric and Remote Sensing, Val d'Isere, France, 17-21 January 1994.
91. Ranson, K. J., and Sun, G., 1994, Mapping biomass of a northern forest using multifrequency SAR data.IEEE Transactions on Geoscience and Remote Sensing, 32, 388-396.
92. Ranson, K. J., Sun, G., Weishampel, J. F., and Knox, R. G., 1997, Forest biomass from combined ecosystem and radar backscatter modeling. Remote Sensing of Environment, 59, 118-133.
93. Rignot, E., Way, J., WiLLiams, C., and Viereck, L., 1994, Radar estimates of above ground biomass in boreal forest of interior Alaska.IEEE Transactions on Geoscience and Remote Sensing, 32, 1117-1124.
94. Rohani, K., and Manry, M. T., 1991, The design of multi-layer perceptrons using building blocks.Proceedings of International Joint Conference on Neural Networks (IJCNN) 91, Seattle, Washington(Piscataway, NJ: IEEE), pp. 497- 502.
95. Rohani, K., and Manry, M. T., 1994, Multi-layer neural network design based on a modular concept.Journal of Artificial Neural Networks, 1, 349-370.
96. Ross, J. Kand Marshak, A. L., 1989, Influence of leaf orientation and the specular component of leaf reflectance on the canopy bidirectional reflectance.Remote Sensing of Environment, 24, 213-225.
97. Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., and Harlan, J. C., 1974, Monitoring the vernal advancement of retrogradation of natural vegetation. Type III, Final report, NASA/GSFC, Greenbelt, Maryland, USA.
98. Ruck, D. W., 1990, The multilayer perceptron as an approximation to a Bayes optimal discriminant function.IEEE Transactions on Neural Networks, 1, 296-306.
99. RumeLhart, D. J., Hinton, G. E., and WiLLiams, R. J., 1986, Learning representations by back-propagation errors.Nature, 323, 533-536.
100. Saatchi, S. S., and Moghaddam, M., 1994, Biomass distribution in boreal forest using SAR imagery.Proceedings of the Society of Photo-Optical Instrumentation Engineers, 2314, 437-447.
101. Simpson, G., 1994, Crop yield prediction using a CMAC neural network.Proceedings of the Society of Photo-Optical Instrumentation Engineers, 2315, 160-171.
102. Smith, J. A., 1993, LAI inversion using a back-propagation neural network trained with a multiple scattering model.IEEE Transactions on Geoscience and Remote Sensing, 31, 1102-1106.
103. Subbarayan, S., Kim, K. K., Manry, M. T., Devarajan, V., and Chen, H.-H., 1996, Modular neural network architecture using piece-wise linear mapping.Conference Record of the Thirtieth Annual Asilomar Conference on Signals, Systems, and Computers, 3-6 November 1996(Piscataway, NJ: IEEE), vol. 2, pp. 1171-1175.
104. Sun, G., and Ranson, K. J., 1995, A three-dimensional radar backscatter model of forest canopies, IEEE Transactions on Geoscience and Remote Sensing, 33, 372-382.
105. Tukey, J., 1977, Exploratory Data Analysis(Reading, MA: Addison-Wesley).
106. ULaby, F. T., ALLen, C. T., Eger, G., and Kanemasu, E., 1984, Relating microwave backscatter coefficients to leaf area index.Remote Sensing of Environment, 14, 113-133.
107. ULaby, F. T., Sarabandi, K., McDonaLd, M., Whitt, M., and Dobson, M. C., 1990, Michigan microwave canopy scattering model (MIMICS).International Journal of Remote Sensing, 11, 1123-1253.
108. Verhoef, W., 1984, Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model.Remote Sensing of Environment, 16, 125-141.
109. VoLterra, V., 1887, Sopra le funzioni che dipendono da altre funzioni.Rend. Regia Academia dei Lince, 20 Sem., pp. 97-105, 141-146, 153-158.
110. Wan, E. A., 1990, Neural network classification: a Bayesian interpretation.IEEE Transactions on Neural Networks, 1, 303-305.
111. Wang, Y., and Dong, D., 1997, Retrieving forest stand parameters from SAR backscatter data using a neural network trained by a canopy backscatter model.International Journal of Remote Sensing, 18, 981-989.
112. Wasserman, P. D., 1989, Neural Computing: Theory and Practice(New York: Van Norstrand Reinhold), p. 230.
113. Werbos, P. J., 1974, Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD Thesis, Harvard University, Cambridge, MA, USA.
114. Werbos, P. J., 1994, The Roots of Backpropagation: From Ordered Derivative to Neural Networks and Political Forecasting(New York: Wiley).
115. Yu, Q., A pollo, S. J., and M anry, M. T., 1993, MAP estimation and the multilayer perceptron.Proceedings of the 1993 IEEE Workshop on Neural Networks for Signal Processing, Linthicum Heights, Maryland, 6-9 September 1993, pp. 30-39.
116. Zornetzer, S. F., Davis, J. L., and Lau, C., 1990, An Introduction to Neural and Electronic Networks(New York: Academic).