A Detailed Comparison of Backpropagation Neural Network and Maximum-Likelihood Classifiers for Urban Land Use Classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 33, Issue 4, 1995, Pages 981-996

  Paola, Justin D ^a   Schowengerdt, Robert A ^a  

^a UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; FUNCTIONS; ITERATIVE METHODS; PATTERN RECOGNITION; VECTORS;

ACTIVATION FUNCTION; BACKPROPAGATION NEURAL NETWORK; MAXIMUM LIKELIHOOD CLASSIFIERS; MULTISPECTRAL IMAGE CLASSIFICATION; SIGMOID FUNCTION;

NEURAL NETWORKS;

EID: 0029341018     PISSN: 01962892     EISSN: 15580644     Source Type: Journal    
DOI: 10.1109/36.406684     Document Type: Article

References (33)

1. J. R. Anderson, E. E. Hardy, J. T. Roach, and R. E. Witmer, “A land use and land cover classification system for use with remote sensor data,” USGS Professional Paper No 964, U.S. Geologic Survey, U.S. Government Printing Office, Washington, DC, 1976.
2. J. A. Benediktsson, P. H. Swain, and O. K. Ersoy, “Neural network approaches versus statistical methods in classification of multisource remote sensing data,” IEEE Trans. Geosci. Remote Sensing, vol. 28, pp. 540–551, July 1990.
3. J. A. Benediktsson, P. H. Swain, O. K. Ersoy, and D. Hong, “Classification of very high dimensional data using neural networks,” in Proc. IGARSS, College Park, MD, May 1990, pp. 1269–1272.
4. H. Bischof, W. Schneider, and A. J. Pinz, “Multispectral classification of landsat-images using neural networks,” IEEE Trans. Geosci. Remote Sensing, vol. 30, pp. 482–490, May 1992.
5. M. Caudill, “Neural networks primer: Part III,” AI Expert, June, 1988, pp. 53–59.
6. D. L. Civco, “Landsat TM image classification with an artificial neural network,” in Proc. ASPRS-ACSM Ann. Meeting, Baltimore, MD, vol. 3, pp. 67–77, 1991.
7. D. L. Civco, “Artificial neural networks for land-cover classification and mapping,” Int. J. Geographical Inform. Syst., vol. 7, no. 2, pp. 173–186, 1993.
8. M. S. Dawson, A. K. Fung, and M. T. Manry, “Sea ice classification using fast learning neural networks,” in Proc. IGARSS, Houston, TX, May 1992, pp. 1070–1071.
9. P. Dreyer, “Classification of land cover using optimized neural nets on SPOT data,” Photogrammetric Eng. Remote Sensing, vol. 59, no. 5, pp. 617–621, 1993.
10. G. M. Foody, N. A. Campbell, N. M. Trodd, and T. F. Wood, “Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification,” Photogrammetric Eng. Remote Sensing, vol. 58, no. 9, pp. 1335–1341, 1992.
11. H. Gish, “A probabilistic approach to the understanding and training of neural network classifiers,” in Proc. IEEE Int. Conf. Acoust., Signal, Speech Processing, Albuquerque, NM, vol. 3, 1990, pp. 1361–1364.
12. S. Haykin, Neural Networks: A Comprehensive Foundation. New York, NY: Macmillan, 1994.
13. P. D. Heermann and N. Khazenie, “Classification of multispectral remote sensing data using a back-propagation neural network,” IEEE Trans. Geosci. Remote Sensing, vol. 30, pp. 81–88, Jan. 1992.
14. D. R. Hush and B. G. Horne, “Progress in supervised neural networks,” IEEE Signal Processing Mag., vol. 10, no. 1, pp. 8–39, 1993.
15. I. Kanellopoulos, A. Varfis, G. G. Wilkinson, and J. Mégier, “Neural network classification of multi-date satellite imagery,” in Proc. IGARSS, Espoo, Finland, June 1991, pp. 2215–2218.
16. I. Kanellopoulos, G. G. Wilkinson, and J. Mégier, “Integration of neural network and statistical image classification for land cover mapping,” in Proc. IGARSS, Tokyo, Japan, Aug. 1993, pp. 511–513.
17. J. Key, J. A. Maslanik, and A. J. Schweiger, “Classification of merged AVHRR and SMMR arctic data with neural networks,” Photogrammetric Eng. Remote Sensing, vol. 55, no. 9, pp. 1331–1338, 1989.
18. J. Key, J. A. Maslanik, and A. J. Schweiger, “Neural network versus maximum likelihood classifications of spectral and textural features in visible, thermal, and passive microwave data,” in Proc. IGARSS, College Park, MD, May 1990, pp. 1277–1280.
19. R. K. Kiang, “Classification of remotely sensed data using OCR-inspired neural network techniques,” in Proc. IGARSS, Houston, TX, May 1992, pp. 1081–1083.
20. H. Li, Z. Liu, and W. Sun, “A new approach to pattern recognition of remote sensing image using artificial neural network,” in Proc. IGARSS, Tokyo, Japan, Aug. 1993, pp. 713–715.
21. R. P. Lippmann, “An introduction to computing with neural networks,” IEEE Acoust., Speech, Signal Processing Mag., Apr. 1987, pp. 4–22.
22. Z. K. Liu and J. Y. Xiao, “Classification of remotely-sensed image data using artificial neural networks,” Int. J. Remote Sensing, vol. 12, no. 11, pp. 2433–2438, 1991.
23. G. E. McClellan, R. N. DeWitt, T. H. Hemmer, L. N. Matheson, and G. O. Moe, “Multispectral image-processing with a three-layer backpropagation network,” in Proc. 1989 Int. Joint Conf. Neural Networks, Washington, DC, vol. 1, pp. 151–153, 1989.
24. G. Mehldau and R. A. Schowengerdt, “A C-extension for rule-based image classification systems,” Photogrammetric Eng. Remote Sensing, vol. 56, no. 6, pp. 887–892, 1990.
25. N.J. Mulder and L. Spreeuwers, “Neural networks applied to the classification of remotely sensed data,” in Proc. IGARSS, Espoo, Finland, June 1991, pp. 2211–2213.
26. Y.-H. Pao, Adaptive Pattern Recognition and Neural Networks. Reading, MA: Addison-Wesley, 1989.
27. J. D. Paola and R. A. Schowengerdt, “A review and analysis of backpropagation neural networks for classification of remotely sensed multispectral imagery,” Int. J. Remote Sensing, to be published.
28. M. D. Richard and R. R. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Computation, vol. 3, pp. 461–483, 1991.
29. N. D. Ritter and G. F. Hepner, “Application of an artificial neural network to land-cover classification of thematic mapper imagery,” Computers Geosci., vol. 16, no. 6, pp. 873–880, 1990.
30. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in D. E. Rumelhart and J. L. McClelland, Eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. Cambridge, MA: MIT Press, 1986, pp. 318–362.
31. N. Short, “A real-time expert system and neural network for the classification of remotely sensed data,” in Proc. ASPRS-ACSM Ann. Meet., Baltimore, MD, vol. 3, pp. 406–418, 1991.
32. P. H. Swain, “Fundamentals of pattern recognition in remote sensing,” in P. H. Swain and S. M. Davis, Eds., Remote Sensing: The Quantitative Approach. New York: McGraw-Hill, 1978, pp. 137–187.
33. S. W. Wharton, “A context-based land-use classification algorithm for high-resolution remotely sensed data,” J. Appl. Photographic Eng., vol. 8, no. 1, pp. 46–50, 1982.