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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. Comm. ACM 37, 77–84 (1994)
Bezdek, J.C., Pal, S.K.: Fuzzy Models For Pattern Recognition: Methods that Search for Structures in Data. IEEE Press, New York (1992)
Zadeh, L.A.: Fuzzy sets. Inform. Contr. 8, 338–353 (1965)
Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE Acoust., Speech, Signal Processing Mag. 4, 4–22 (1987)
Slowinski, R. (ed.): Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992)
Yasdi, R.: Combining Rough Sets Learning and Neural Learning Method to Deal with Uncertain and Imprecise Information. Neuro-Computing 7, 61–84 (1995)
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)
Sarkar, M., Yegnanarayana, B.: Rough-Fuzzy Membership Functions. In: Proc. IEEE World Congress on Computational Intelligence, Alaska, USA, vol. 1, pp. 796–801 (1998)
Jensen, J.R.: Introductory Digital Image Processing, a Remote Sensing Perspective, 2nd edn. Prentice Hall series in Geographic Information Science (1995)
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)
Wang, F.: Fuzzy Supervised Classification of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 28, 194–201 (1990)
Pal, S., Mitra, S.: Multi-layer Perceptron, Fuzzy Sets, and Classification. IEEE Transaction on Neural Networks 3, 683–697 (1992)
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)
Tso, B., Mather, P.M.: Classification Methods for Remotely Sensed Data. CRC Press, Boca Raton (2001)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumar, N., Agrawal, A. (2006). Nonparametric Neural Network Model Based on Rough-Fuzzy Membership Function for Classification of Remotely Sensed Images. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_10
Download citation
DOI: https://doi.org/10.1007/11949619_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
eBook Packages: Computer ScienceComputer Science (R0)