Biblioteca de la Universidad Complutense de Madrid

Spectral fuzzy classification: An application.


Amo, Ana del y Montero, Javier y Fernández, Angela y López, Marina y Tordesillas, José Manuel y Biging, Greg (2002) Spectral fuzzy classification: An application. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 32 (1). pp. 42-48. ISSN 1094-6977

[img] PDF
Restringido a Sólo personal autorizado del repositorio hasta 2020.


URL Oficial:


Geographical information (including remotely sensed data) is usually imprecise, meaning that the boundaries between different phenomena are fuzzy. In fact, many classes in nature show internal gradual differences in species, health, age, moisture, as well other factors. If our classification model does not acknowledge that those classes are heterogeneous, and crisp classes are artificially imposed, a final careful analysis should always search for the consequences of such an unrealistic assumption. In this correspondence, we consider the unsupervised algorithm presented in [3], and its application to a real image in Sevilla province (south Spain). Results are compared with those obtained from the ERDAS ISO-DATA classification program on the same image, showing the accuracy of our fuzzy approach. As a conclusion, it is pointed out that whenever real classes are natural fuzzy classes, with gradual transition between classes, then its fuzzy representation will be more easily understood-and therefore accepted-by users.

Tipo de documento:Artículo
Palabras clave:Fuzzy classification; Outranking models; Remote sensing
Materias:Ciencias > Informática > Inteligencia artificial
Ciencias > Matemáticas > Lógica simbólica y matemática
Código ID:16777

A. Amo, D. Gómez, J. Montero, and G. Biging, “Relevance and redundancy in fuzzy classification systems,” Mathw. Soft Comput. 8, pp. 203–216, 2001.

A. Amo, E. Molina, and J. Montero, “Representation of consistent recursive rules,” Eur. J. Oper. Res., vol. 130, pp. 29–53, 2001.

A. del Amo, J. Montero, and G. Biging, “Classifying pixels by means of fuzzy relations,” Int. J. General Syst., vol. 29, pp. 605–621, 2000.

A. Amo, J. Montero, and V. Cutello, “On the principles of fuzzy classification,” in Proc. Int. Conf. North American Information Processing Society. Piscataway, NJ, 1999, pp. 675–679.

G. H. Ball and D. J. Hall, “ISODATA—A novel method of data analysis and pattern classification,” Stanford Res. Inst., Menlo Park, CA, 1965.

“A clustering technique for sumarizing multivariate data,” Behav. Sci., vol. 12, pp. 153–155, 1967.

“PROMENADE—An on-line pattern recognition system,” Stanford Res. Inst., Menlo Park, CA, 1969.

J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms.New York: Plenum, 1981.

J. C. Bezdek and S. K. Pal, Eds., Fuzzy Models for Pattern Recognition. New York: IEEE Press, 1992.

G. Bortolan and W. Pedrycz, “Reconstruction problem and information granularity,” IEEE Trans. Fuzzy Syst., vol. 5, pp. 234–248, May 1997.

V. Cutello, J. Montero , and E. Molina, “Associativeness versus recursiveness,” in Proc. 26th Int. Symp. Multiple-Valued Logic, Santiago de Compostela, Spain, 1996, pp. 154–159.

G. M. Foody, “The continuum of classification fuzziness in thematic mapping,” Photogramm. Eng. Remote Sens., vol. 65, pp. 443–451, 1999.

A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: A review,” ACM Comput. Surv., vol. 31, pp. 264–323, 1999.

J. R. Jensen, Introductory Digital Image Processing. A Remote Sensing Perspective. Englewood Cliffs, NJ: Prentice-Hall, 1996.

A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic. New York: Van Nostrand, 1985.

S. A. Orlovski, “Decision-making with a fuzzy preference relation,” Fuzzy Sets Syst., vol. 6, pp. 169–195, 1978.

B. J. Park, W. Pedrycz, and S. K. Oh, “Identification of fuzzy models with the aid of evolutionary data granulation,” Proc. IEEE, Control Theory and Applications, vol. 148, pp. 406–418, Sep. 2001.

A. D. Pearman, J. Montero, and J. Tejada, “Fuzzy multicriteria decisión support for budget allocation in the transport sector,” TOP, vol. 3, pp. 47–68, 1995.

P. P. Perny and B. Roy, “The use of fuzzy outranking relations in preference modeling,” Fuzzy Sets Syst., vol. 49, pp. 33–53, 1992.

A. Salski, O. Fränzle, and P. Kandzia, “Fuzzy logic in ecological modeling,” Ecol. Mod., vol. 85, 1995.

J. Siskos, J. Lochard, and J. Lombard, “A multicriteria decision making methodology under fuzziness: Application to the evaluation of radiological protection in nuclear power plants,” in Fuzzy Sets and Decision Analysis, H. J. Zimmermann, L. A. Zadeh, and B. R. Gaines, Eds. Amsterdam, The Netherlands: North Holland, 1984, pp. 261–264.

R. R. Yager, “Quasi-associative operations in the combination of evidence,” Kybernetes, vol. 16, pp. 37–41, 1987.

“On ordered weighted averaging aggregation operators in multicriteria decision making,” IEEE Trans. Syst., Man, Cybern., vol. 18, pp. 183–190, Jan./Feb. 1988.

L. A. Zadeh, “Fuzzy sets,” Inf. Contr., vol. 8, pp. 338–353, 1965.

“Similarity relations and fuzzy orderings,” Inf. Sci., vol. 3, pp. 177–200, 1971.

“Fuzzy sets and information granularity,” in Advances in Fuzzy Sets Theroy and Applications, R. K. Ragade, M. M. Gupta, and R. R. Yager, Eds. Amsterdam, The Netherlands: North-Holland, 1979, pp. 3–18.

Depositado:22 Oct 2012 10:13
Última Modificación:21 Abr 2016 17:11

Sólo personal del repositorio: página de control del artículo