A Multi-objective Approach for Building Hyperspectral Remote Sensed Image Classifier Combiners

  • S. L. J. L. Tinoco
  • D. Menotti
  • J. A. dos Santos
  • G. J. P. Moreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9019)


Hyperspectral images are one of the most important data source for land cover analysis. These images encode information about the earth surface expressed in terms of spectral bands, allowing us to precisely classify and identify materials of interest. An approach that has been widely used is the combination of various classification methods in order to produce a more accurate thematic map based on classification of hyperspectral images. Our multi-objective remote sensed hyperspectral image classifier combiner (MORSHICC) approach uses a genetic algorithm-based strategy for choosing the best subset of classifiers, that is, the one which provides higher accuracy with the fewest possible amount of classifiers. We propose to use combiners that linearly weigh each classification approach through Genetic Algorithm (WLC-GA) and Integer Linear Programming (WLC-ILP). For building the combiners, we used three data representations and four learning algorithms, producing twelve classification approaches such that the multi-objective approach can select the best subset. Experimental results on well-known datasets show that the MORSHICC approach with WLC-GA and WLC-IP not only produces combiners with fewer classifier approaches but also improves the final accuracy rates. Therefore, these combiners may produce more accurate thematic maps for real and large datasets in a short time.


Remote Sensing 


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  1. 1.
    Benediktsson, J., Palmason, J., Sveinsson, J.: Classification of hyperspectral data from urban areas based on extrended morphological profiles. IEEE Trans. on Geoscience and Remote Sensing (TGARS) 43(3), 480–491 (2005)CrossRefGoogle Scholar
  2. 2.
    Benediktsson, J.A., Chanussot, J., Fauvel, M.: Multiple classifier systems in remote sensing: From basics to recent developments. In: Benediktsson, J.A., Chanussot, J., Fauvel, M. (eds.) Multiple Classifier Systems. LNCS, vol. 4472. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Benediktsson, J.A., Chanussot, J., Moon, W.M.: Very high-resolution remote sensing: Challenges and opportunities [point of view]. Proceedings of the IEEE 100(6), 1907–1910 (2012)CrossRefGoogle Scholar
  4. 4.
    Brown, G., Kuncheva, L.I.: “Good” and “Bad” diversity in majority vote ensembles. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 124–133. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  5. 5.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. on Information Theory 13(1), 21–27 (January 1967)Google Scholar
  6. 6.
    Deb, K.: Multi-objective optimisation using evolutionary algorithms: an introduction. In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, pp. 3–34. Wiley (2011)Google Scholar
  7. 7.
    Dos Santos, J.A., Faria, F.A., da S Torres, R., Rocha, A., Gosselin, P.H., Philipp-Foliguet, S., Falcao, A.: Descriptor correlation analysis for remote sensing image multi-scale classification. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3078–3081 (2012)Google Scholar
  8. 8.
    Faria, F.A., dos Santos, J.A., Rocha, A., da, S., Torres, R.: A framework for selection and fusion of pattern classifiers in multimedia recognition. Pattern Recognition Letters 39, 52–64 (2014)CrossRefGoogle Scholar
  9. 9.
    Gabrys, B., Ruta, D.: Genetic algorithms in classifier fusion. Applied soft computing 6(4), 337–347 (2006)CrossRefGoogle Scholar
  10. 10.
    Ghosh, A., Shankar, B., Bruzzone, L., Meher, S.: Neuro-fuzzy-combiner: an effective multiple classifier system. Int. J. of Knowledge Engineering and Soft Data Paradigms 2(2), 107–129 (2010)CrossRefGoogle Scholar
  11. 11.
    Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P.: Moderate diversity for better cluster ensembles. Information Fusion 7(3), 264–275 (2006)CrossRefGoogle Scholar
  12. 12.
    ILOG S.A.: CPLEX 12.5 User’s Manual (2012)Google Scholar
  13. 13.
    Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)Google Scholar
  14. 14.
    Licciardi, G., Marpu, P., Chanussot, J., Benediktsson, J.: Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters (GRSL) 9(3), 447–451 (2012)CrossRefGoogle Scholar
  15. 15.
    Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. on Geoscience and Remote Sensing (TGARS) 42(8), 1778–1790 (2004)CrossRefGoogle Scholar
  16. 16.
    Plaza, A., et al.: Recent advances in techniques for hyperspectral image processing. Remote Sensing Environment 113(1), 110–122 (2009)CrossRefGoogle Scholar
  17. 17.
    Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)CrossRefGoogle Scholar
  18. 18.
    Prasad, S., Bruce, L.M., Chanussot, J.: Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques, vol. 3. Springer (2011)Google Scholar
  19. 19.
    Santos, A.B., de, A., Araújo, A., Menotti, D.: Combining multiple classification methods for hyperspectral data interpretation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(3), 1450–1459 (2013)CrossRefGoogle Scholar
  20. 20.
    Santos, A.B., de S. Celes, C.S.F., de A.Arajo, A., Menotti, D.: Feature selection for classification of remote sensed hyperspectral images: a filter approach using genetic algorithm and cluster validity. In: The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), vol. 2, pp. 675–681 (2012)Google Scholar
  21. 21.
    Tinôco, S., Santos, A., Santos, H., dos Santos, J.A., Menotti, D.: Ensemble of classifiers for remote sensed hyperspectral land cover analysis: An approach based on linear programming and weighted linear combination. In: IEEE Int. Geoscience and Remote Sensing Symposium (IGARSS), pp. 4082–4085 (2013)Google Scholar
  22. 22.
    Zhang, L., Zhang, L., Tao, D., Huang, X.: On combining multiple features for hyperspectral remote sensing image classification. IEEE Trans. on Geoscience and Remote Sensing (TGARS) 50(3), 879–893 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • S. L. J. L. Tinoco
    • 1
  • D. Menotti
    • 1
  • J. A. dos Santos
    • 2
  • G. J. P. Moreira
    • 1
  1. 1.Computing DepartmentUniversidade Federal de Ouro PretoOuro PretoBrazil
  2. 2.Computer Science DepartmentUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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