Classification of Multisensor Remote-Sensing Images by Structured Neural Networks

IEEE Transactions on Geoscience and Remote Sensing

Volumn 33, Issue 3, 1995, Pages 562-578

  Serpico, Sebastiano B ^a   Roli, Fabio ^a  

^a UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR BACKPROPAGATION; MULTISENSOR REMOTE SENSING IMAGES; STRUCTURED NEURAL NETWORKS; TRIAL AND ERROR PROCESS;

ALGORITHMS; BACKPROPAGATION; CLASSIFICATION (OF INFORMATION); MATHEMATICAL MODELS; NEURAL NETWORKS; PHOTOINTERPRETATION; PROBLEM SOLVING;

REMOTE SENSING;

EID: 0029307334     PISSN: 01962892     EISSN: 15580644     Source Type: Journal    
DOI: 10.1109/36.387573     Document Type: Article

References (39)

1. Proc. Int. Workshop on “Multisource Data Integration in Remote Sensing for Land Inventory Applications,” IAPR TC7, Delft, The Netherlands, Wageningen Agricultural Univ. Pub., Sept. 7–9, 1992.
2. S. E. Decatur, “Applications of neural networks to terrain classification,” in Proc. Int. Joint Conf. Neural Networks, Washington D.C., 1989, vol. 1, pp. 283–288.
3. 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. 540–552, July 1990.
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. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation. Reading, MA: Addison Wesley, 1991.
6. J. Lee, R. C. Weger, S. K. Sengupta, and R. M. Welch, “A neural network approach to cloud classification,” IEEE Trans. Geosci. Remote Sensing, vol. 28, pp. 846–855, Sept. 1991.
7. J. W. Shavlik, R. J. Mooney, and G. G. Towell, “Symbolic and neural learning algorithms: An experimental comparison,” Machine Learning, vol. 6, pp. 111–143, 1991.
8. G. P. Drago and S. Ridella, “Statistically controlled activation weight initialization (SCAWI),” IEEE Trans. Neural Networks, vol. 3, pp. 627–631, July 1992.
9. B. Chandrasekaran and A. Goel, “From numbers to symbols to knowledge structures: Artificial intelligence perspectives on the classification task,” IEEE Trans. Syst. Man Cybernet., vol. 18, no. 3, May/June 1988, pp. 415–424.
10. G. E. McClellan, R. N. De Witt, T. H. Hemmer, L. N. Matheson, and G. O. Moe, “Multispectral image-processing with a tree-layer backpropagation network,” in Proc. Int. Joint Conf. Neural Networks, Washington DC, 1989, vol. 1, pp. 151–153.
11. 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.
12. Y. Salu and J. Tilton, “Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods,” IEEE Trans. Geosci. Remote Sensing, vol. 31, pp. 606–617, May 1993.
13. O. K. Ersoy and D. Hong, “Parallel, self-organizing, hierarchical neural networks, IEEE Trans. Neural Networks, vol. 1, pp. 167–178, June 1990.
14. J. A. Benediktsson, O. K. Ersoy, and P. H. Swain, “A consensual neural network, in Proc. 1991 Int. Geosci. Remote Sensing Symp., Espoo, Finland, June 3–6, 1991, pp. 2219–2222.
15. J. N. Hwang, S. R. Lay, and R. Kiang, “Robust construction neural networks for classification of remotely sensed data,” in Proc. World Congress on Neural Networks, Portland, OR, July 11–15, 1993, vol. 4, pp. 580–584.
16. M. R. Azimi-Sadjadi, S. Ghaloum, and R. Zoughi, “Terrain classification in sar images using principal components analysis and neural networks,” IEEE Trans. Geosci. Remote Sensing, vol. 31, no. 2, pp. 511–515, Mar. 1993.
17. L. Mascarilla, E. H. Zahazah, and J. Desachy, “Neural networks classifiers based on geocoded data and multispectral images for satellite image interpretation,” in Proc. 5th Int. Conf., CAIP '93, Budapest, Hungary, 1993, pp. 830–837.
18. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixture of local experts,” Neural Computation, vol. 3, pp. 79–87, 1991.
19. H. Bischof, A. Pinz, and W. G. Kropatsch, “Visualization methods for neural networks,” in Proc. 11th Int. Conf. Pattern Recognition, 11th IAPR, The Hague, The Netherlands, Aug. 30-Sept. 3, 1992, pp. 581–585.
20. K. J. Cios and N. L. Ning, “A machine learning method for generation of a neural network architecture: A continuous ID3 algorithm,” IEEE Trans. Neural Networks, vol. 3, no. 2, pp. 280–291, Mar. 1992.
21. R. C. Lacher, S. I. Hruska, and D. C. Kuncicky, “Back-propagation learning in expert networks,” IEEE Trans. Neural Networks, vol. 3, pp. 62–72, Jan. 1992.
22. R. M. Goodman, C. M. Higgins, J. W. Miller, and P. Smyth, “Rule-based neural networks for classification and probability estimation,” Neural Computation, vol. 4, pp. 781–804, 1992.
23. R. Krishnapuram and J. Lee, “Fuzzy-set-based hierarchical networks for information fusion in computer vision,” Neural Networks, vol. 5, no. 2, pp. 335–350, 1992.
24. R. P. Lippmann, “An introduction to computing with neural networks,” IEEE Acoust., Speech Signal Processing Mag., vol. 4, pp. 4–22, Apr. 1987.
25. D. E. Rumelhart and J. L. McClelland et al., Parallel Distributed Processing, vol. 1–2. Cambridge, MA: MIT Press, 1986.
26. G. E. Hinton, “Preface to the special issue on connectionist symbol processing,” Artificial Intelligence, vol. 46, no. 1–2, pp. 1–4, Nov. 1990.
27. J. N. Hwang and H. Li, “Interactive query learning for isolated speech recognition,” IEEE International Workshop on Neural Networks for Signal Processing. Denmark: IEEE Press, Sept. 1992, pp. 93–102.
28. C. H. Chen and G. H. You, “Class-sensitive neural network,” Neural Parallel & Scientific Computations, vol. 1. Silver Spring, MD: Dynamic Publ.: 1993, pp. 93–96.
29. H. Gish, “A probabilistic approach to the understanding and training of neural network classifiers,” in Proc. 1990 Int. Conf. Acoust., Speech, Signal Processing, Apr. 3–6, 1990, pp. 1361–1364.
30. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. New York: Academic Press, 1990.
31. N. J. Nilsson, The Mathematical Foundations of Learning Machines. San Mateo, CA: Morgan Kaufmann, 1990.
32. S. B. Serpico, F. Roli, P. Pellegretti, and G. Vernazza, “Structured neural networks for the classification of multisensor remote-sensing images,” Int. Geosci. Remote Sensing Symp., Tokyo, vol. 2, Aug. 1993, pp. 907–909.
33. G. Jacucci, F. Roli, S. B. Serpico, and G. Vernazza, “Land cover classification in remote sensing images using structured neural networks,” in Proc. Int. EARSeL Workshop Satellite Tech. GIS Mediterranean Forest Mapping Fire Management, Thessaloniki, Greece, Nov. 4–6, 1993, pp. 435–446.
34. Proc. Final Workshop MAESTRO-1 Europ. SAR Campaign, ESTEC, Noordwijk, The Netherlands, European Space Agency Pub. Mar. 6–7, 1992.
35. P. J. Hancock, “Data representation in neural nets: An empirical study,” in Touretsky, Hinton, and Senjnowki, Eds., Proc. 1988 Connectionist Models Summer School, San Mateo, CA: Morgan Kaufmann, 1988, pp. 11–20.
36. E. B. Baum and D. Haussler, “What size network gives valid generalization,” Neural Computation, vol. 1, pp. 151–160, 1989.
37. R. O. Duda and P. E. Hart, Patter Classification and Scene Analysis. Wiley, 1973.
38. F. Wang, “Fuzzy supervised classification of remote sensing images,” IEEE Trans. on Geosci. Remote Sensing, vol. 28, pp. 194–201, Mar. 1990.
39. R. C. Luo and M. Gray, “Multisensor integration and fusion in intelligent systems,” IEEE Trans. Syst. Man Cybernet., vol. 19, pp. 901–931, Sept./Oct. 1989.