Advertisement

Nonparametric Neural Network Model Based on Rough-Fuzzy Membership Function for Classification of Remotely Sensed Images

  • Niraj Kumar
  • Anupam Agrawal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

A nonparametric neural network model based on Rough-Fuzzy Membership function, multilayer perceptron, and back-propagation algorithm is described. The described model is capable to deal with rough uncertainty as well as fuzzy uncertainty associated with classification of remotely sensed multi-spectral images. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of rough fuzzy class membership values. This allows efficient modeling of indiscernibility and fuzziness between patterns by appropriate weights being assigned to the back-propagated errors depending upon the Rough-Fuzzy Membership values at the corresponding outputs. The effectiveness of the model is demonstrated on classification problem of IRS-P6 LISS IV images of Allahabad area. The results are compared with statistical (Minimum Distance), conventional MLP, and FMLP models.

Keywords

Membership Function Equivalence Class Kappa Coefficient Neural Network Method Training Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)MATHGoogle Scholar
  2. 2.
    Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. Comm. ACM 37, 77–84 (1994)CrossRefGoogle Scholar
  3. 3.
    Bezdek, J.C., Pal, S.K.: Fuzzy Models For Pattern Recognition: Methods that Search for Structures in Data. IEEE Press, New York (1992)Google Scholar
  4. 4.
    Zadeh, L.A.: Fuzzy sets. Inform. Contr. 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE Acoust., Speech, Signal Processing Mag. 4, 4–22 (1987)Google Scholar
  6. 6.
    Slowinski, R. (ed.): Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992)MATHGoogle Scholar
  7. 7.
    Yasdi, R.: Combining Rough Sets Learning and Neural Learning Method to Deal with Uncertain and Imprecise Information. Neuro-Computing 7, 61–84 (1995)MATHGoogle Scholar
  8. 8.
    Czyzewski, A., Kaczmarek, A.: Speech Recognition Systems Based on Rough Sets and Neural Networks. In: Proc. 3rd Wkshp. Rough Sets and Soft Computing (RSSC 1994), San Jose, CA, pp. 97–100 (1994)Google Scholar
  9. 9.
    Sarkar, M., Yegnanarayana, B.: Rough-Fuzzy Membership Functions. In: Proc. IEEE World Congress on Computational Intelligence, Alaska, USA, vol. 1, pp. 796–801 (1998)Google Scholar
  10. 10.
    Jensen, J.R.: Introductory Digital Image Processing, a Remote Sensing Perspective, 2nd edn. Prentice Hall series in Geographic Information Science (1995)Google Scholar
  11. 11.
    Gopal, S., Fischer, M.: A Comparison of Three Neural Network Classifiers for Remote Sensing Classification. IEEE Geoscience and Remote Sensing Symposium 1, 787–789 (1996)Google Scholar
  12. 12.
    Wang, F.: Fuzzy Supervised Classification of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 28, 194–201 (1990)CrossRefGoogle Scholar
  13. 13.
    Pal, S., Mitra, S.: Multi-layer Perceptron, Fuzzy Sets, and Classification. IEEE Transaction on Neural Networks 3, 683–697 (1992)CrossRefGoogle Scholar
  14. 14.
    Benediktsson, J.A., Sveinsson, J.R.: Multisource Remote Sensing Data Classification Based on Consensus and Pruning. IEEE Transactions on Geoscience and Remote Sensing 41, 932–936 (2003)CrossRefGoogle Scholar
  15. 15.
    Tso, B., Mather, P.M.: Classification Methods for Remotely Sensed Data. CRC Press, Boca Raton (2001)MATHCrossRefGoogle Scholar
  16. 16.
    Melgani, F., Al Hashemy, B.A.R., Taha, S.M.R.: An Explicit Fuzzy Supervised Classification Method for Multispectral Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 38, 287–295 (2000)CrossRefGoogle Scholar
  17. 17.
    Paola, J.D., Schowengerdt, R.A.: Detailed Comparison of Backpropagation Neural Network and Maximum-Likelihood Classifiers for Urban Land Use Classification. IEEE Transactions on Geoscience and Remote Sensing 33, 981–996 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Niraj Kumar
    • 1
  • Anupam Agrawal
    • 1
  1. 1.Indian Institute of Information TechnologyAllahabad

Personalised recommendations