Summary
In recent years, the remote-sensing community has became very interested in applying neural networks to image classification and in comparing neural networks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classification algorithm can be regarded as a “panacea”. The superiority of one algorithm over the other strongly depends on the selected data set and on the efforts devoted to the “designing phases” of algorithms. In this paper, we propose the use of “ensembles” of neural and statistical classification algorithms as an alternative approach based on the exploitation of the complementary characteristics of different classifiers. Classification results provided by image classifiers contained in these ensembles are “merged” according to statistical combination methods. Experimental results on a multi-sensor remote-sensing data set point out that the use of classifiers ensembles can constitute a valid alternative to the development of new classification algorithms “more complex” than the present ones. In particular, we show that the combination of results provided by statistical and neural algorithms provides classification accuracies better than the ones obtained by single classifiers after long “designing phases”.
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References
J. A. Benediktsson, P. H. Swain, and O. K. Ersoy, “Neural network approaches versus statistical methods in classification of multi-source remote-sensing data”, IEEE Transactions on Geoscience and Remote Sensing, vol. 28, no. 4, pp. 540–552, 1990.
F. Roli, S. B. Serpico, and G. Vernazza, “Neural Networks for Classification of Remotely Sensed Imagesi”,Fuzzy Logic and Neural Network Handbook,Part2, Chapter15, McGraw-Hill Series on Computer Eng., C. H. Chen Editor, pp. 15.1–15.28, 1996.
L. Bruzzone, C. Conese, F. Maselli, and F. Roli, “Multisource classification of complex rural areas by statistical and neural-network approaches”, Photogram-metric Engineering and Remote Sensing, 1997, in press.
S. B. Serpico, and F. Roli, “Classification of multi-sensor remote-sensing images by structured neural networks”, IEEE Transactions on Geoscience and Remote Sensing,vol. 33, no. 3, pp. 562–578, 1995.
I. Kanellopoulos, G. G. Wilkinson, and J. Mégier, “Integration of neural network and statistical image classification for land cover mapping”, in Proceedings of the International Geoscience and Remote Sensing Symposium, (IGARSS 93), Tokyo,18–21 August 1993, vol. II, pp. 511–513.
S. E. Decatur, “Applications of neural networks to terrain classification”, in Proceedings International Joint Conference on Neural Networks 89, Washington D.C., vol. 1, pp. 283–288, 1989.
J. Lee, R. C. Weger, S. K. Sengupta, and R. M. Welch, “A neural network approach to cloud classification”, IEEE Transactions on Geoscience and Remote Sensing, vol. 28, no. 5, pp. 846–855 1991.
H. Bischof, W. Schneider, and A. J. Pinz, “Multispectral classification of Landsat-images using neural networks”, IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 3, pp. 482–490, 1992.
M. R Azimi-Sadjadi, S. Ghaloum, and R. Zoughi, “Terrain Classification in Sax Images Using Principal Components Analysis and Neural Networks”, IEEE Transactions on Geoscience and Remote Sensing,vol. 31, no. 2, pp. 511–515, 1993.
Y. Salu, and J. Tilton, “Classification of multispectral image data by the Binary Diamond neural network and by non-parametric, pixel-by-pixel methods”, IEEE Transactions on Geoscience and Remote Sensing, vol. 31, no. 3, pp. 606–617, 1993.
K. Fukunaga, Introduction to Statistical Pattern Recognition,Academic Press, Inc., New York, 2nd edition, 1990.
L. Xu, A. Krzyzak, and C. Y. Suen, “Methods for combining multiple classifiers and their applications to handwriting recognition”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, no. 3, pp. 418–435, 1992.
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© 1997 Springer-Verlag Berlin Heidelberg
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Roli, F., Giacinto, G., Vernazza, G. (1997). Comparison and Combination of Statistical and Neural Network Algorithms for Remote-Sensing Image Classification. In: Kanellopoulos, I., Wilkinson, G.G., Roli, F., Austin, J. (eds) Neurocomputation in Remote Sensing Data Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59041-2_13
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DOI: https://doi.org/10.1007/978-3-642-59041-2_13
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