Abstract
This paper deals with the problem of how input data normalization can affect the performances of the Counterpropagtion neural network. In the following, an example drawn from the landcover classification of remotely sensed images is presented and a solution, based on the Decorrelation Stretching technique, is proposed.
Similar content being viewed by others
References
R.H. Nielsen. Counterpropagation networks,Applied Optics, vol. 26, no. 23, pp. 4979–4984, 1987.
P.D. Wasserman.Neural Computing, Theory and Practice, Van Nostrand Reinhold, New York, 1989.
A.R. Gillespie, A.B. Kahle, R.E. Walker. Color enhancement of highly correlated images — I — Decorrelation stretching and HSI contrast stretches.Remote Sensing of Environment, vol. 20, pp. 209–235, 1986.
C. Bechini, L. Chiarantini, P. Ciotti, S. Moretti, E Pettinelli, N. Pierdicca. MAC'19 on Montespertoli: preliminary assessment of land polarimetric features,Proc. IGARSS, vol. 1, pp. 395–397, 1992.
P. Coppo, P. Ferrazzoli, G. Luzi, S. Paloscia, G. Schiavon, C. Susini. MAC'91 on Montespertoli: preliminary analysis of multifrequency SAR sensitivity to soli and vegetation paramenters,Proc. IGARSS, Vol. 1, pp. 489–491, 1992.
G. G. Wilkinson. The processing and interpretation of remotely-sensed satellite imagery: a current view,Remote Sensing and Geographial Information Systems for Resource Management in Developing Countries, pp. 71–96, 1991.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Chiuderi, A. Improving the Counterpropagation network performances. Neural Process Lett 2, 27–30 (1995). https://doi.org/10.1007/BF02312353
Issue Date:
DOI: https://doi.org/10.1007/BF02312353