A Boosted Genetic Fuzzy Classifier for land cover classification of remote sensing imagery

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 66, Issue 4, 2011, Pages 529-544

  Stavrakoudis, D G ^a   Theocharis, J B ^a   Zalidis, G C ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

AdaBoost; Genetic fuzzy rule based classification systems (GFRBCS); Local feature selection; Multispectral image classification; Textural and spatial features

Indexed keywords

ADABOOST; GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS (GFRBCS); LOCAL FEATURE SELECTION; MULTISPECTRAL IMAGE CLASSIFICATION; SPATIAL FEATURES;

ADAPTIVE BOOSTING; FUZZY RULES; FUZZY SYSTEMS; IMAGE CLASSIFICATION; LANDFORMS; REMOTE SENSING; SPACE OPTICS;

FEATURE EXTRACTION;

FUZZY MATHEMATICS; GENETIC ALGORITHM; IKONOS; LAKE ECOSYSTEM; LAND CLASSIFICATION; LAND COVER; MULTISPECTRAL IMAGE; REMOTE SENSING; WETLAND;

EID: 79957870587     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2011.01.010     Document Type: Article

References (61)

1. Alcalá-Fdez J., Sánchez L., García S., del Jesus M.J., Ventura S., Garrell J.M., Otero J., Romero C., Bacardit J., Rivas V.M., Fernández J.C., Herrera F. KEEL: A software tool to assess evolutionary algorithms to data mining problems. Soft Computing 2009, 13(3):307-318. http://www.keel.es.
2. Bäck T. Evolutionary Algorithms in Theory and Practice 1996, Oxford University Press, Oxford.
3. Bandyopadhyay S., Maulik U., Mukhopadhyay A. Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 2007, 45(5):1506-1511.
4. Bárdossy A., Samaniego L. Fuzzy rule-based classification of remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing 2002, 40(2):362-374.
5. Benediktsson J.A., Swain P.H., Esroy O.K. Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. International Journal of Remote Sensing 1993, 14(15):2883-2903.
6. Beyer H.G., Schwefel H.P. Evolution strategies-A comprehensive introduction. Natural Computing 2002, 1(1):3-52.
7. Bezdek J.C., Kuncheva L.I. Nearest prototype classifier designs: An experimental study. International Journal of Intelligent Systems 2001, 16(12):1445-1473.
8. Breiman L. Random Forests. Machine Learning 2001, 45(1):5-32.
9. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth Inc.
10. Casillas J., Martínez P., Benítez A.D. Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems. Soft Computing 2008, 13(5):451-465.
11. Civco, D.L., 1991. Landsat TM image classification with an artificial neural network. In: Proceedings ACSM/ASPRS, Baltimore, MD, pp. 67-77.
12. Civco D.L. Artificial neural networks for land-cover classification and mapping. International Journal of Geographical Information Systems 1993, 7(2):173-186.
13. Cohen W.W. Fast effective rule induction. Machine Learning: Proceedings of the Twelfth International Conference 1995, 1-10. Morgan Kaufmann, Lake Tahoe, California.
14. Cordón O., del Jesús M.J., Herrera F., Lozano M. MOGUL: a methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. International Journal of Intelligent Systems 1999, 14(11):1123-1153.
15. Cordón O., Gomide F., Herrera F., Hoffmann F., Magdalena L. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 2004, 141(1):5-31.
16. Cordón O., Herrera F. A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples. International Journal of Approximate Reasoning 1997, 17(4):369-407.
17. Cordón O., Herrera F. A two-stage evolutionary process for designing TSK fuzzy rule-based systems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 1999, 29(6):703-715.
18. Cordón O., Herrera F. Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems. Fuzzy Sets and Systems 2001, 118(2):235-255.
19. Cordón O., Herrera F., Hoffmann F., Magdalena L. Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases 2001, World Scientific, Singapore.
20. Cortes C., Vapnik V. Support vector networks. Machine Learning 1995, 20(3):273-297.
21. del Jesus M.J., Hoffmann F., Navascués L.J., Sánchez L. Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Transactions on Fuzzy Systems 2004, 12(3):296-308.
22. Duda R., Hart P., Stork D. Pattern Classification 2001, John Wiley & Sons Inc, New York. 2nd ed.
23. Fernández A., García S., del Jesus M.J., Herrera F. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data sets. Fuzzy Sets and Systems 2008, 159(18):2378-2398.
24. Fernández A., Herrera F., del Jesus M.J. On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets. Expert Systems With Applications 2009, 36(6):9805-9812.
25. Freund, Y., Schapire, R., 1996. Experiments with a new boosting algorithm. In: Proc. 13th International Conference on Machine Learning, pp. 148-156.
26. Ghosh A., Pal N.R., Das J. A fuzzy rule based approach to cloud estimation. Remote Sensing of Environment 2006, 100(4):531-549.
27. González A., Herrera F. Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware and Soft Computing 1997, 4(3):233-249.
28. González A., Pérez R. Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets and Systems 1998, 96(1):37-51.
29. González A., Pérez R. SLAVE: a genetic learning system based on an iterative approach. IEEE Transactions on Fuzzy Systems 1999, 7(2):176-191.
30. González A., Pérez R. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 2001, 31(3):417-425.
31. Haralick R.M., Shapiro L.G. Robot and Computer Vision: Vol. 1 1992, Addison-Wesley, Reading, Mass.
32. Hoffmann F. Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets and Systems 2004, 141(1):47-58.
33. Horne, J.H., 2003. A tasseled cap transformation for IKONOS images. In: Proc. ASPRS Annual Conference, Anchorage, Alaska, May.
34. Ishibuchi H., Nakashima T., Murata T. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 1999, 29(5):601-618.
35. Ishibuchi H., Nakashima T., Murata T. Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences 2001, 136(1-4):109-133.
36. Ishibuchi H., Nojima Y. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. International Journal of Approximate Reasoning 2007, 44(1):4-31.
37. Ishibuchi H., Nozaki K., Yamamoto N., Tanaka H. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems 1995, 3(3):260-270.
38. Ishibuchi H., Yamamoto T. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems 2004, 141(1):59-88.
39. Ishibuchi H., Yamamoto T., Nakashima T. Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 2005, 35(2):359-365.
40. Kononenko I. Estimating attributes: Analysis and extensions of RELIEF. Lecture Notes in Computer Science 1994, vol. 784:171-182. Springer, Berlin/Heidelberg. F. Bergadano, L. De Raedt (Eds.).
41. Laha A., Pal N.R., Das J. Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory. IEEE Transactions on Geoscience and Remote Sensing 2006, 44(6):1633-1641.
42. Lin C.T. Neural Fuzzy Control Systems with Structure and Parameter Learning 1994, World Scientific, Singapore.
43. Lin C.T., Lee Y.C., Pu H.C. Satellite sensor image classification using cascaded architecture of neural fuzzy network. IEEE Transactions on Geoscience and Remote Sensing 2000, 38(2 II):1033-1043.
44. Liu Z., Liu A., Wang C., Niu Z. Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Future Generation Computer Systems 2004, 20(7):1119-1129.
45. Mallat S.G. Theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989, 11(7):674-693.
46. Mansoori E.G., Zolghadri M.J., Katebi S.D. SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data. IEEE Transactions on Fuzzy Systems 2008, 16(4):1061-1071.
47. 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 2000, 38(1 I):287-295.
48. Mitrakis N.E., Topaloglou C.A., Alexandridis T.K., Theocharis J.B., Zalidis G.C. A novel self-organizing neuro-fuzzy multilayered classifier for land cover classification of a VHR image. International Journal of Remote Sensing 2008, 29(14):4061-4087.
49. Mitrakis N.E., Topaloglou C.A., Alexandridis T.K., Theocharis J.B., Zalidis G.C. Decision fusion of GA self-organizing neuro-fuzzy multilayered classifiers for land cover classification using textural and spectral features. IEEE Transactions on Geoscience and Remote Sensing 2008, 46(7):2137-2152.
50. Nakashima T., Schaefer G., Yokota Y., Ishibuchi H. A weighted fuzzy classifier and its application to image processing tasks. Fuzzy Sets and Systems 2007, 158(3):284-294.
51. Pakhira M.K., Bandyopadhyay S., Maulik U. A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets and Systems 2005, 155(2):191-214.
52. Pal M., Foody G.M. Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing 2010, 48(5):2297-2307.
53. Papadakis S.E., Theocharis J.B. A GA-based fuzzy modeling approach for generating TSK models. Fuzzy Sets and Systems 2002, 131(2):121-152.
54. Parodi, A, Bonelli, P., 1993. A new approach to fuzzy classifier systems. In: Proc. 5th International Conference on Genetic Algorithms, University of Illinois, Urbana-Champaign, July 17-21, pp. 223-230.
55. Otero J., Sánchez L. Induction of descriptive fuzzy classifiers with the Logitboost algorithm. Soft Computing 2006, 10(9):825-835.
56. Quinlan J.R. C4.5: Programs for Machine Learning 1993, Morgan Kauffman.
57. Stathakis D., Vasilakos A. Comparison of computational intelligence based classification techniques for remotely sensed optical image classification. IEEE Transactions on Geoscience and Remote Sensing 2008, 44(8):2305-2318.
58. Thrift P. Fuzzy logic synthesis with genetic algorithms. Proc. Fourth International Conference on Genetic Algorithms (ICGA'91) 1991, 509-513. Morgan Kaufmann, Los Altos, CA.
59. Tseng M.H., Chen S.J., Hwang G.H., Shen M.Y. A genetic algorithm rule-based approach for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing 2008, 63(2):202-212.
60. Valenzuela-Rendón, M., 1991. The fuzzy classifier system: A classifier system for continuously varying variables. In: Proc. 4th International Conference on Genetic Algorithms, University of California, San Diego, July 13-16, pp. 346-353.
61. Zhang Y., Hong G. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images. Information Fusion 2005, 6(3):225-234.