This chapter presents a Boosted Genetic Fuzzy Classifier (BGFC), for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. BGFC's learning algorithm is divided into two stages. During the first stage, a number of fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. The rule base generation stage is followed by a genetic tuning stage, aiming at improving the cooperation among the fuzzy rules and, subsequently, increasing the classification performance attained after the former stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. The results indicate that the proposed system is able to handle multi-dimensional feature spaces, effectively exploiting information from different feature sources.
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References
R. Duda, P. Hart and D. Stork, Pattern Classification, 2nd ed. Wiley, New York, 2001.
D. Stathakis and A. Vasilakos, Comparison of computational intelligence based classification techniques for remotely sensed optical image classification, IEEE. Trans. Geosci. Remote Sensing 44(8), 2305–2318, 2008.
J.A. Benediktsson, P.H. Swain and O.K. Esroy, Conjugate gradient neural networks in classification of multisource and very-highdimensional remote sensing data, Int. J. Remote Sens. 14, 2883–2903, 1993.
Z. Liu, A. Liu, C. Wang and Z. Niu, Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification, Futur. Gener. Comp. Syst. 20(7), 1119– 1129, 2004.
C.-T. Lin, Y.-C. Lee and H.-C. Pu, Satellite sensor image classification using cascaded architecture of neural fuzzy network, IEEE Trans. Geosci. Remote Sensing, 38(2), 1033–1043, 2000.
N.E. Mitrakis, C.A. Topaloglou, T.K. Alexandridis, J.B. Theocharis and G.C. Zalidis, A novel self-organizing neuro-fuzzy multilayered classifier for land cover classification of a VHR image, Int. J. Remote Sens. 29(14), 4061–4087, 2008.
N.E. Mitrakis, C.A. Topaloglou, T.K. Alexandridis, J.B. Theocharis and G.C. Zalidis, Decision fusion of GA self-organizing neuro-fuzzy multilayered classifiers for land cover classification using textural and spectral features, IEEE Trans. Geosci. Remote Sensing 46(7), 2137–2152, 2008.
A. Bárdossy and L. Samaniego, Fuzzy rule-based classification of remotely sensed imagery, IEEE Trans. Geosci. Remote Sensing 40, 362–374, Feb. 2002.
A. Laha, N.R. Pal and J. Das, Land cover classification using fuzzy rules and aggregation of contextueal information through evidence theory, IEEE Trans. Geosci. Remote Sensing 44(6), 1633–1641, 2006.
A. Ghosh, N.R. Pal and J. Das, A fuzzy rule based approach to cloud estimation, Remote Sens. Environ. 100, 531–549, 2006.
F. Melgani, B.A.R. Al Hashemy and S.M.R. Taha, An explicit fuzzy supervised classification method for multispectral remote sensing images, IEEE Trans. Geosci. Remote Sensing 38(1), 287–295, 2000.
O. Cordón, F. Herrera, F. Hoffmann and L. Magdalena, Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, World Scientific, Singapore, 2001.
O. Cordón, F. Gomide, F. Herrera, F. Hoffmann and L. Magdalena, Ten years of genetic fuzzy systems: Current framework and new trends, Fuzzy Sets Syst. 141, 5–31, 2004.
A. González and R. Pérez, SLAVE: A genetic learning system based on an iterative approach, IEEE Trans. Fuzzy Syst. 7(2), 176–191, 1999.
A. González and R. Pérez, Completeness and consistency conditions for learning fuzzy rules, Fuzzy Sets Syst. 96, 37–51, 1998.
H. Ishibuchi, T. Nakashima and T. Murata, Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems, IEEE Trans. Syst. Man Cybern, Part B — Cybern. 29, 601–618, 1999.
T. Bäck, Evolutionary Algorithms in Theory and Practice, Oxford University Press, Oxford, 1996.
O. Cordón and F. Herrera, A two-stage evolutionary process for designing TSK fuzzy rule-based systems, IEEE Trans. Syst. Man Cybern, Part B — Cybern. 29(6), 703–715, 1999.
O. Cordón and F. Herrera, Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems, Fuzzy Sets Syst. 118(2), 235– 255, 2001.
F. Hoffmann, Combining boosting and evolutionary algorithms for learning of fuzzy classification rules, Fuzzy Sets Syst. 14, 47–58, 2004.
P. Thrift, Fuzzy logic synthesis with genetic algorithms, in Proc. Fourth Int. Conf. on Genetic Algorithms (ICGA′91), San Diego, USA, Morgan Kaufmann, Los Altos, CA, pp. 509–513, 1991.
J. Casillas, P. Martínez and A.D. Benítez, Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems, Soft Comput., in press, 2008.
S.E. Papadakis and J.B. Theocharis, A GA-based fuzzy modeling approach for generating TSK models, Fuzzy Sets Syst. 131(1), 121–152, 2002.
H. Ishibuchi, K. Nozaki, N. Yamamoto and H. Tanaka, Selecting fuzzy if-then rules for classification problems using genetic algorithms, IEEE Trans. Fuzzy Syst. 3(3), 260–270, 1995.
M. Valenzuela-Rendón, The fuzzy classifier system: A classifier system for continuously varying variables, in Proc. 4th Int. Conf. Genetic Algorithms, University of California, San Diego, July 13–16, pp. 346–353, 1991.
A. Parodi and P. Bonelli, A new approach to fuzzy classifier systems, in Proc. 5th Int. Conf. Genetic Algorithms, University of Illinois, Urbana-Champaign, July 17–21, pp. 223–230, 1993.
A. González and F. Herrera, Multi-stage genetic fuzzy systems based on the iterative rule learning approach, Mathware Soft Comput. 4, 233–249, 1997.
O. Cordón and F. Herrera, A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples, Int J. Approx. Reasoning 17(4), 369–407, 1997.
O. Cordón, M.J. del Jesús, F. Herrera and M. Lozano, MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach, Int. J. Intell. Syst. 14(11), 1123–1153, 1999.
A. González and R. Pérez, Selection of relevant features in a fuzzy genetic learning algorithm, IEEE Trans. Syst. Man Cybern, Part B — Cybern. 31(3), 417–425, 2001.
M.J. del Jesus, F. Hoffmann, L.J. Navascués and L. Sánchez, Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms, IEEE Trans. Fuzzy Syst. 12(3), 296–308, 2004.
Y. Freund and R. Schapire, Experiments with a new boosting algorithm, in Proc. 13th Int. Conf. Machine Learning, pp. 148–156, 196.
C.T. Lin, Neural Fuzzy Control Systems with Structure and Parameter Learning. World Scientific, Singapore, 1994.
Y. Zhang and G. Hong, An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images, Information Fusion 6, 225– 234, 2005.
J. H. Horne, A tasseled cap transformation for IKONOS images, in Proc. ASPRS Annu. Conf., Anchorage, Alaska, 2003.
R.M. Haralick and L.G. Shapiro, Robot and Computer Vision, Vol. 1, Addison-Wesley, Reading, MA, 1992.
S.G. Mallat, Theory for multiresolution signal decomposition: The wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693, July 1989.
C. Cortes and V. Vapnik, Support vector networks, Mach. Learn. 20, 273–297, 1995.
J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kauffman, 1993.
J.C. Bezdek and L.I. Kuncheva, Nearest prototype classifier designs: An experimental study, Int. J. Intell. Syst. 16(12), 1445–1473, 2001.
J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández and F. Herrera, KEEL: A software tool to assess evolutionary algorithms to data mining problems, Soft Comput. 13(3), 307–318, 2009. Software available online: http://www.keel.es
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Stavrakoudis, D.G., Theocharis, J.B., Zalidis, G.C. (2009). Genetic Fuzzy Rule-Based Classifiers for Land Cover Classification from Multispectral Images. In: Valavanis, K.P. (eds) Applications of Intelligent Control to Engineering Systems. Intelligent Systems, Control, and Automation: Science and Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3018-4_8
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