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A way to improve an architecture of neural network classifier for remote sensing applications

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Abstract

Recent results in neural network research have demonstrated their utility in a variety of application areas. Neural networks are able to achieve a very high performance, and classification accuracy in real world applications such as handwritten character recognition, remote sensing images, vision, robotic. Network performance greatly depends not only on the input/output data, but also on its architecture. Most of neural network applications have been developed using anad hoc approach resulting in poor efficiency and performance. In this paper, a development method of neural network applications is presented, and illustrated with a neural classifier of remote sensing images. It is shown how to create in an iterative way a neural classifier architecture, and how to refine a network organization using performance evaluation criteria.

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References

  1. J. A. Benediktson. Neural network approaches in classification of multisources remote sensing data,IEEE Trans. on Geosc. and Remote Sensing, vol. 28, pp. 540–555, 1990.

    Google Scholar 

  2. R. P. Gorman. Analysis of hidden units in a layered network trained to classify sonar,Neural Networks, vol. 1, pp. 75–89, 1988.

    Article  Google Scholar 

  3. N. D. Ritter, G. F. Hepner. Application of an artificial neural network to land cover classification of thematic mapper imagery,Comp. Geosc., vol. 6, pp. 873–880, 1990.

    Google Scholar 

  4. J.J. Korczak. An interactive image processing system for the Macintosh environment,Proc. PUCC (Vancover), 1990.

  5. P. Suetens. Computational strategies for object recognition,ACM Comp.Survey, vol. 24, 1992.

  6. J.J. Korczak, F. Hammadi. An approach to design neural network architecture,Research Report, Louis Pasteur University, Strasbourg, 1991.

    Google Scholar 

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Korczak, J., Hammadi-Mesmoudi, F. A way to improve an architecture of neural network classifier for remote sensing applications. Neural Process Lett 1, 13–16 (1994). https://doi.org/10.1007/BF02312395

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  • DOI: https://doi.org/10.1007/BF02312395

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