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Neural Networks in Multiple Classifier Systems for Remote-Sensing Image Classification

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Soft Computing in Image Processing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 210))

Abstract

In recent years there has been a growing interest in the development of supervised classification techniques with higher classification reliability of satellite images. The superiority of one technique over the others cannot be claimed. Many experimental results showed that the classification accuracy depends more on the particular application than on the technique chosen to perform the task. Moreover in many applications it is very difficult to design a classification system that exhibits the required accuracy for the final classification product. Therefore a new technique is emerging that considers multiple classifier systems (MCSs) instead of a single classification technique. Neural networks can participate effectively in a MCS in several ways. This chapter focuses on the role of neural networks in MCSs, which can be either: (1) an individual classifier among the classifiers ensemble, (2) the fusion center that integrates the decisions of individual classifiers, (3) the selector that picks some classifiers’ decisions and ignores the others, (4) or the selector and fuser at the same time.

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References

  1. Battiti R. and Colla A.M. (1994), Democracy in neural nets: Voting schemes for classification, Neural Networks, vol. 7, pp.691-707.

    Article  Google Scholar 

  2. Benediktsson J. and Kanellopoulos I. (1999), Classification of Multisource and Hyperspectral Data Based on Decision Fusion, IEEE Tran. Geosci. Remote Sensing, vol. 37, pp. 1367-1377.

    Article  Google Scholar 

  3. Ceccarelli M. and Petrosino A. (1997), Multi-feature adaptive classifiers for SAR image segmentation, Neurocomputing, vol.14, pp. 345-363.

    Article  Google Scholar 

  4. Cho S.B. and Kim J.H. (1995), Combining multiple neural networks by fuzzy integral and robust classification, IEEE Transactions on Systems, Man, and Cybernetics, vol. 25, pp. 380-384.

    Article  Google Scholar 

  5. Farag A. A., Mohamed R. M. and Mahdi H. (2002), Experiments in Image Classification and Data Fusion, Proceedings of 5th International Conference on Information Fusion, Annapolis, MD, vol. 1, pp. 299-308.

    Google Scholar 

  6. Giacinto G. and Roli F. (1997), Adaptive Selection of Image Classifiers, Proc. of the 9th ICIAP, Lecture Notes in Computer Science 1310, Springer Verlag, pp.38-45.

    Google Scholar 

  7. Giacinto G. and Roli F. (2001), Dynamic classifier selection based on multiple classifier behavior, Pattern Recognition, vol. 34, pp. 1879-1881.

    Article  MATH  Google Scholar 

  8. Hashem S., Schmeiser B. and Yih Y. (1994), Optimal linear combinations of neural networks: an overview, IEEE International Conference on Neural Networks, pp.1507-1512.

    Google Scholar 

  9. Ho T.H., Hull J. J. and Srihari S.N. (1994), Decision Combination in Multiple Classifier System, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, pt. 1, pp. 66-75.

    Google Scholar 

  10. Jain A.K., Duin R.P.W. and Mao J. (2000), Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp.4-37.

    Article  Google Scholar 

  11. Kamel M. S., Wanas N. M. (2001), Decision Fusion in Neural Network Ensembles, International Joint Conference on neural Networks (IJCNN’01), Washington D.C., USA, vo.l 4, pp. 2952-2957, Jul 15-19.

    Google Scholar 

  12. Kuncheva L.I. (1993), Change-glasses approach in pattern recognition, Pattern Recognition Letters, vol. 14, pp. 619-623.

    Article  Google Scholar 

  13. Kuncheva L.I. (2002), Switching between selection and fusion in combining classifiers: An experiment, IEEE transactions on Systems Man and Cybernetics, Part B-cybernetics, vol. 32, pp. 146-156.

    Article  Google Scholar 

  14. Kuncheva L.I., Bezedek J.C. and Dubin R.P.W. (2001), Decision Templates for Multiple Classifier Fusion: an Experimental Comparison, Pattern Recognition, vol. 34, pp. 299-314.

    Article  MATH  Google Scholar 

  15. Lam L. and Suen C.Y. (1995), Optimal combination of pattern classifiers, Pattern Recognition Letters, vol. 16, pp. 945-954.

    Article  Google Scholar 

  16. Rastrigin L.A. and Erenstein R.H. (1981), Method of Collective Recognition, Energoizdat, Moscow, In Russian.

    Google Scholar 

  17. Rogova G. (1994), Combining the results of several neural network classifiers, Neural Networks, vol. 7, pp. 777-781.

    Article  Google Scholar 

  18. Verikasa A., Lipnickas A. and Malmqvista K. (1999), Soft combination of neural classifiers: A comparative study, Pattern Recognition Letters, vol. 20, pp. 429-444.

    Article  Google Scholar 

  19. Woods K., Kegelmeyer W. Ph. and Bowyer K. (1997), Combination of Multiple Classifiers Using Local Accuracy Estimates, IEEE Tran. Trans. Pattern Analysis and machine Intelligence, vol. 19, pp. 405-41.

    Article  Google Scholar 

  20. Chuanyi J. and Sheng M. (1997), Combinations of Weak Classifiers, IEEE Trans. Neural Networks, vol. 8, No. 1.

    Google Scholar 

  21. Duda R. O., Hart P. E. and Stork D. (2001), Pattern Classification”, 2nd edition, Wiley.

    Google Scholar 

  22. ERDAS (1999), ERDAS field guide, ERDAS, Inc., 5th edition.

    Google Scholar 

  23. Fischer M.M., Gopal S., Staufer P. and Steinocher K. (1997), Evaluation of neural pattern classifiers for a remote sensing application, Geographical Systems, vol. 35, no.2, pp. 308-325.

    Google Scholar 

  24. Grabisch M. (1995), Fuzzy integral in multi-criteria decision making, Fuzzy Sets and Systems, vol.69, pp. 279-298.

    Article  MATH  MathSciNet  Google Scholar 

  25. Hornik, K. (1989), Multilayer Feedforward Networks are Universal Approximators, Neural Networks, Vol. 2, pp. 359-366.

    Article  Google Scholar 

  26. Jacobs R.A. (1995), Methods for combining experts’ probability assessments, Neural Computation, vol 7, pp. 867-888.

    Google Scholar 

  27. Kittler J., Hatef M., Duin R.P.W. and J. Matas (1997), On Combining Classifier, IEEE Transactions on Pattern Analysis and machine Intelligence, vol. 20, pp. 226-239.

    Article  Google Scholar 

  28. Kohavi R. and Wolpert DH. (1996), Bias plus variance decomposition for zero-one loss function, Machine Learning: Proc. 13th Int. Conf., Morgan Kaufmann, pp. 275-283.

    Google Scholar 

  29. Kuncheva L.I, Bezdek J., and Sutton M. (1998), On combining multiple classifiers by fuzzy templates, In: Proceedings of the 1998 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS’98, Pensacola FL, pp. 193-197.

    Google Scholar 

  30. Kuncheva L.I. (2005), Diversity in multiple classifier systems, Information Fusion, 6, pp. 3-4.

    Article  Google Scholar 

  31. Lam L. and Suen C.Y. (1997), Application of Majority Voting to Pattern Recognition: an Analysis of Its Behavior and Performance, IEEE Transactions on Systems, Man, and Cybernetics, vol. 27, No. 5.

    Google Scholar 

  32. Lam L. (2000), Classifier combinations: Implementations and theoretical issues, In Multiple Classifier Systems. First International Workshop, MCS 2000, Cagliari, Italy, vol.1857 of Lecture Notes in Computer Science, Springer-Verlag, pp. 77-86.

    Google Scholar 

  33. Lee Y. (1991), Handwritten Digit Recognition Using K-Nearest-Neigbor, Radial-Basis Functions, and Backpropagation Neural Network, Neural Computation, vol.3, pp. 440-449.

    Google Scholar 

  34. Moody J.E. and Darken C.J (1989), Fast Learning in Networks of Locally-Tuned Processing Units, Neural Computation, vol. 1, pp. 281-294.

    Article  Google Scholar 

  35. Munro P. and Parmanto B. (1997), Competition among networks improves committee performance, In Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, pp. 592-598.

    Google Scholar 

  36. Perkins T.C. (2000), Remote sensing image classification and fusion for terrain reconstruction, MSc. Thesis, University of Louisville, KY.

    Google Scholar 

  37. Petrakos M., Benediktsson J. and Kanellopoulos I. (2001), The Effect of Classifier Agreement on the Accuracy of the Combined Classifier in Decision Level Fusion, IEEE Tran. Geosci. Remote Sensing, vol. 39, pp. 2539-2546.

    Article  Google Scholar 

  38. Poggio T. and Girosi F. (1990), Networks for Approximation and Learning, Proceedings of the IEEE, vol. 78, pp. 1481-1497.

    Google Scholar 

  39. Roli F. and Giacinto G. (2002), Design of Multiple Classifier Systems, in H.Bunke and A Kandel (Eds.), Hybrid methods in Pattern recognition, World scientific Publishing.

    Google Scholar 

  40. Specht D. F. (1990), Probabilistic neural network, Neural Networks, vol. 3, pp. 109-118.

    Article  Google Scholar 

  41. Taniguchi M. and Tresp V. (1997), Averaging regularized estimators, Neural Computation, vol. 9, pp. 1163-1178.

    Article  Google Scholar 

  42. Tumer K. and Ghosh J. (1999), Linear and Order Statistics Combiners for Pattern Classification, In: Sharkey, A.J.C. (ed.): Combining Artificial Neural Nets, Springer, pp. 127-161.

    Google Scholar 

  43. Wolpert D. (1992), Stacked generalization, Neural Networks, vol. 5, pp. 241-259.

    Article  Google Scholar 

  44. Xu L., Krzyzak A. and Suen C.Y. (1992), Methods of combining multiple classifiers and their application to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 418-435.

    Article  Google Scholar 

  45. Lipnickas A. and Korbicz J. (2003), Adaptive selection of neural networks for a committee decision, IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application, Lviv, Ukraine, pp. 109-114.

    Google Scholar 

  46. Wilkinson G. (2005), Results and implications of a study of fifteen years of satellite image classification experiments, IEEE Tran. Geosci. Remote Sensing, vol. 43, no. 3, pp. 433-440.

    Article  Google Scholar 

  47. Kumar A.S., Basu S.K. and Majumdar K.L. (1997), Robust classification of multispectral data using multiple neural networks and fuzzy integral. IEEE Tran. Geosci. Remote Sensing, vol 35, pp. 287-790.

    Article  Google Scholar 

  48. Kumar A. S. and Majumder K. L. (2001), Information fusion in tree classifiers. Int. Journal of Remote Sensing, vol. 22, no. 5, pp. 861-869.

    Article  Google Scholar 

  49. Tso B. C. K. and Mather P. M. (1999), Classification of multisource remote sensing imagery using a genetic algorithm and Markov random fields. IEEE Trans. Geosci. Remote Sensing, vol. 37, no. 3, pp. 1255-1260.

    Article  Google Scholar 

  50. Seong J. C. and Usery E. L. (2001), Fuzzy image classification for continental scale multitemporal NDVI series images using invariant pixels and an image stratification method. Photogramm. Eng. Remote Sensing, vol. 67, no. 3, pp. 287-294.

    Google Scholar 

  51. Zhang J. and Foody G. M. (1998), A fuzzy classification of sub-urban land cover from remotely sensed imagery. Int. J. Remote Sensing, vol. 19, no. 14, pp. 2721-2738.

    Article  Google Scholar 

  52. Giacinto G., Roli F., and Bruzzone L. (2000), Combination of neural and statistical algorithms for supervised classification of remote-sensing images. Pattern Recognition Letters, vol. 21, no. 5, pp. 385-397.

    Article  Google Scholar 

  53. Giacinto G. and Roli F. (1997), Ensembles of Neural Networks for Soft Classification of Remote Sensing Images. Proc. of the European Symposium on Intelligent Techniques, Bari, Italy, pp. 166-170, March 20-21.

    Google Scholar 

  54. Petersen M. E., de Ridder D. and Handels H. (2002), Image processing using neural networks - a review. Pattern Recognition, Vol. 35, No. 10, pp. 2279-301.

    Article  MATH  Google Scholar 

  55. Gamba P. and Houshmand B. (2001), An efficient neural classification chain of SAR and optical urban images. Int. J. Remote Sensing, vol. 22, no. 8, pp. 1535-1553.

    Article  Google Scholar 

  56. Yoshida T. and Omatu S. (1994), Neural network approach to land cover map**. IEEE Trans. Geosci. Remote Sensing, vol. 32, no. 5, pp. 1103-1109.

    Article  Google Scholar 

  57. Bischof H., Schneider W., and Pinz A. J. (1992), Multispectral classification of Landsat images using neural networks. IEEE Trans. Geosci. Remote Sensing, vol. 30, no. 3, pp. 482-490.

    Article  Google Scholar 

  58. Heerman P. D. and Khazenie N. (1992), Classification of multispectral remote sensing data using a backpropagation neural network. IEEE Trans. Geosci. Remote Sensing, vol. 30, no. 1, pp. 81-88.

    Article  Google Scholar 

  59. Atkinson P. M. and Tatnall A. R. L. (1997), Neural networks in remote sensing. Int. J. Remote Sensing, vol. 18, no. 4, pp. 699-709.

    Article  Google Scholar 

  60. Paola J. D. and Schowengerdt R. A. (1995), A review and analysis of backpropagation neural networks for classification of remotely-sensed multispectral imagery. Int. J. Remote Sensing, vol. 16, no. 16, pp. 3033-3058.

    Google Scholar 

  61. Serpico S. B. and Roli F. (1995), Classification of multisensor remote-sensing images by structured neural networks. IEEE Trans. Geosci. Remote Sens., vol. 33, no. 3, pp. 562-578.

    Article  Google Scholar 

  62. Kanellopoulos I. and Wilkinson G. G. (1997), Strategies and best practice for neural network image classification. Int. J. Remote Sensing, vol. 18, no. 4, pp. 711-725.

    Article  Google Scholar 

  63. Ji C. Y. (2000), Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks. Photogramm. Eng. Remote Sensing, vol. 66, no. 12, pp. 1451-1460.

    Google Scholar 

  64. Dreyer P. (1993), Classification of land cover using optimized neural nets on SPOT data. Photogramm. Eng. Remote Sensing, vol. 59, no. 5, pp. 617-621.

    Google Scholar 

  65. Breiman L. (1996), Bagging Predictors. Machine Learning, vol. 24, pp. 123-140.

    MATH  MathSciNet  Google Scholar 

  66. Freund Y. and Schapire R. (1996), Experiments with a new boosting algorithm. Proc. the 13th Int. Conf. on Machine Learning, pp. 148-156.

    Google Scholar 

  67. Partridge D. and Griffith N. (1995), Strategies for improving neural network generalization. Neural Computing and Applications, vol. 3, pp. 27-37.

    Article  MATH  Google Scholar 

  68. Ho T. K. (1998), The random subspace method for constructing decision forests. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, pp. 832-844.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, Assiut University, Assiut, 71516, Egypt

    Moumen T. El-Melegy

  2. National Authority of Remote Sensing and Space Science, Cairo, Egypt

    Safaa M. Ahmed

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  1. Moumen T. El-Melegy
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  2. Safaa M. Ahmed
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Editors and Affiliations

  1. Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 – S9, Gent, 9000, Belgium

    Mike Nachtegael , Dietrich Van der Weken  & Etienne E. Kerre ,  & 

  2. Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, Gent, 9000, Belgium

    Wilfried Philips

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El-Melegy, M.T., Ahmed, S.M. (2007). Neural Networks in Multiple Classifier Systems for Remote-Sensing Image Classification. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Philips, W. (eds) Soft Computing in Image Processing. Studies in Fuzziness and Soft Computing, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-38233-1_3

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  • DOI: https://doi.org/10.1007/978-3-540-38233-1_3

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