Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data

International Journal of Remote Sensing

Volumn 14, Issue 15, 1993, Pages 2883-2903

  Benediktsson, J A ^a   Swain, P H ^b   Ersoy, O K ^b  

^a UNIVERSITY OF ICELAND   (Iceland)

^b PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION; MULTISOURCE DATA; NEURAL NETWORK; VERY HIGH DIMENSIONAL;

CLASSIFICATION (OF INFORMATION); DATA PROCESSING; NEURAL NETWORKS; OPTIMIZATION; STATISTICAL METHODS;

BACKPROPAGATION; CONJUGATE GRADIENT NEURAL NETWORKS; GEOGRAPHIC DATA; MULTISOURCE REMOTE SENSING DATA; VERY HIGH DIMENSIONAL REMOTE SENSING DATA;

REMOTE SENSING;

EID: 0027789411     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431169308904316     Document Type: Article

References (31)

1. ANDERSON, J. A, and ROSENFELD, E. (cds.), 1988, Neurocomputing, (Cambridge: MIT Press).
2. BARNARD, E., and COLE, R. A., 1989, A Neural-Net Training Program Based on Conjugate-Gradient Optimization, Technical Report No. CSE 89-014, Oregon Graduate Center, Beaverton, OR 97006.
3. BENEDIKTSSON, J. A., and SWAIN, P. H., 1990, Statistical Methods and Neural Network Approaches for Classification of Data from Multiple Sources, Technical Report TR-EE 90-64, Laboratory for Applications of Remote Sensing and School of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
4. BENEDIKTSSON, J. A., and SWAIN, P. H., 1992, Consensus theoretic classification methods. I.E.E.E. Transactions on Systems, Man and Cybernetics. 22, 540-552.
5. BENEDIKTSSON, J. A., SWAIN, PH., ERSOY, O. K., and HONG, D., 1990a, Classification of very high dimensional data using neural networks. Proceedings IGARSS ’90 2, (New York: I.E.E.E. Press) pp. 1269-1272.
6. BENEDIKTSSON, J. A., SWAIN, P. H., and ERSOY, O. K., 1990b, Neural network approaches versus statistical methods in classification of muttisource remote sensing data. I.E.E.E. Transactions of Geoscience and Remote Sensing, GE-28, 540-552.
7. DECATUR, S. E., 1989a, Application of neural networks to terrain classification. Proceedings of IJCNN '89, 1, 283-288.
8. Decatur, S. E., 1989b, Application of Neural Networks to Terrain Classification, M. S. thesis, Department of Electrical Engineering and Computer Scicnce, Massachusetts Institute of Technology, Massachusetts.
9. ERSOY, O.K., and HONG, D., 1990, Parallel, self-organizing, hierarchical neural networks.
10. I.E.E.E. Transactions of Neural Networks, 167-178.
11. GOODENOUGH, D. G., GOLDBERG, M., PLUNKETT, G., and ZELEK, J., 1987, The CCRS SAR/MSS Anderson River data set. I.E.E.E. Transactions on Geoscience and Remole Sensing, GE-25, 360-367.
12. GORMAN, R. P., and SKJNOWSKI, T. J., 1988, Analysis of hidden units in a layered network trained to classify sonar signals. Neural Networks, I, 75-90.
13. HEKRMANN, P. H., and KHAZENIE, N., 1990, Application of neural networks for classification of multi-source multi-spectral remote sensing data. Proceedings of IGARSS ’90, 2, 1273-1276.
14. HOFFER, R. M., and STAFF, 1975, Computer-Aided Analysis ofSkylab Multispcctral Scanner Data in Mountainous Terrain for Land Use, Forestry, Water Resources and Geological Applications, LARS Information Note 121275, Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN 47907.
15. HOPPER, R. M., FLEMING, M. D., BARTOLUCCI, L. A., DAVIS, S. M., and NELSON, R. F., 1979, Digital Processing of Landsat MSS and Topographic Data to Improve Capabilities for Computerized Mapping of Forest Cover Types, LARS Technical Report 011579, Laboratory for Applications of Remote Sensing in cooperation with Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907.
16. Jacobs, R. A., 1988, Increased rates of convergence through learning rate adaptation. Neural Networks 1, 295-307.
17. KEREKES, J. P., and LANDGREBE, D. A., 1989, RSSIM: A Simulation Program for Optical Remote Sensing Systems, Technical Report TR-EE 89-48, School of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
18. KOHONEN, T., 1988, An introduction to neural computing. Neural Networks, 1, 3-16.
19. LATHI, B. P., 1983, Modern Digital and Analog Communication Systems, (New York: Holt, Rinehart and Winston.)
20. LEE, C., 1989, Classification algorithms for high dimensional data, Ph.D. thesis proposal, School of Electrical Engineering, Purdue University.
21. LEE, C., BENEDIKTSSON, J. A., and LANDGREBE, D. A., 1992, Feature selection for neural networks using parzen density estimator. Proceedings of IGARSS '92 (New York: I. E.E.E. Press) pp. 839-841.
22. LIPPMAN, R. A., 1987, An introduction to computing with neural nets. I.E.E.E. ASSP Magazine, April, 4-27.
23. Luenberger, D. G., 1984, Linear and Nonlinear Programming, 2nd ed. (Reading: Addison-Wesley).
24. MASLANIK, J., KEY, J., and SCHWEIGER, A., 1990, Neural network identification of sea-ice seasons in passive microwave data. Proceedings of IGARSS ’90 2, (New York: I.E.E.E. Press) 1281-1284.
25. MCCLELLAND, G. E., DEWITT, R. N., HEMMER, T. H., MATHESON, L. H., and MOE, G. O., 1989, Multispectral image processing with a three-layer backpropagation network. Proceedings of IJCNN ’89, 1, (New York: l.E.E.E. Press) pp. 151-153.
26. ROSENBLATT, F., 1958, The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-408.
27. RUMELHART, D. E., HINTON, G. E., and WILLIAMS, R. S., 1986, Learning internal representation by error propagation in Parallel Distributed Processing: Explorations in the Microstructures of Cognition, 1, edited by D. E. Rumelhart and J. L. McClelland (Cambridge: MIT Press), pp. 318-362.
28. SWAIN, P. H., 1978, Fundamentals of pattern recognition in remote sensing. In Remote Sensing-The Quantitative Approach, edited by P. H. Swain and S. M. Davis, (New York: McGraw-Hill Book Company), pp. 136-187.
29. Wan, E. E., 1990, Neural network classification: a Bayesian interpretation. I.E.E.E. Transactions on Neural Networks, 1, 303-305.
30. WATROUS, R. L., 1988, Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization, Technical Report MS-CIS-88-62, LINC LAB 124, University of Pennsylvania, Philadelphia, PA 19104.
31. WIDROW, B., and HOFF, M. E., 1960, Adaptive switching circuits, 1960 IRE WESCON Convention Record (IRE), pp. 96-104.