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Classification of High Dimensional and Imbalanced Hyperspectral Imagery Data

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Pattern Recognition and Image Analysis (IbPRIA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6669))

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Abstract

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate the application order that leads to the best classification performance. Experimental results demonstrate the significance of combining these preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order of application corresponds to first a resampling algorithm and then PCA, this is a question that still needs a much more thorough investigation.

Partially supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596–C04–04/ESP and TIN2009–14205–C04–04, and by Fundacio Caixa Castello–Bancaixa under grant P1–1B2009–04.

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References

  1. Blagus, R., Lusa, L.: Class prediction for high-dimensional class-imbalanced data. Bioinformatics 11(1), 523–540 (2010)

    Google Scholar 

  2. Bruzzone, L., Serpico, S.B.: Classification of imbalanced remote-sensing data by neural networks. Pattern Recogn. Lett. 18(11-13), 1323–1328 (1997)

    Article  Google Scholar 

  3. Camps-Valls, G.: Machine learning in remote sensing data processing. In: Proc. IEEE Int’l. Workshop Machine Learning for Signal Processing, Grenoble, France, pp. 1–6 (2009)

    Google Scholar 

  4. Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    MATH  Google Scholar 

  5. Chen, X., Fang, T., Huo, H., Li, D.: Semisupervised feature selection for unbalanced sample sets of VHR images. IEEE Geosci. Remote Sens. Lett. 7(4), 781–785 (2010)

    Article  Google Scholar 

  6. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

  7. Ezawa, K., Singh, M., Norton, S.: Learning goal oriented bayesian networks for telecommunications risk management. In: Proc. 13th Int’. Conf. Machine Learning, pp. 139–147 (1996)

    Google Scholar 

  8. Fawcett, T., Provost, F.: Adaptive fraud detection. Data Min. Knowl. Disc. 1(3), 291–316 (1997)

    Article  Google Scholar 

  9. García, S., Herrera, F.: Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy. Evol. Comput. 17(3), 275–306 (2009)

    Article  Google Scholar 

  10. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. SIGKDD Explor. Newslett. 11, 10–18 (2009)

    Article  Google Scholar 

  11. Han, H., Wang, W.Y., Mao, B.H.: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In: Proc. Int’l. Conf. Intelligent Computing, Hefei, China, pp. 878–887 (2005)

    Google Scholar 

  12. He, H., Garcia, E.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Article  Google Scholar 

  13. Hsu, P.H., Tseng, Y.H., Gong, P.: Dimension reduction of hyperspectral images for classification applications. Geogr. Inf. Sci. 8(1), 1–8 (2002)

    Google Scholar 

  14. Japkowicz, N., Stephen, S.: The class imbalance problem: A systematic study. Intell. Data Anal. 6(5), 429–449 (2002)

    MATH  Google Scholar 

  15. Kamal, A., Zhu, X., Narayanan, R.: Gene selection for microarray expression data with imbalanced sample distributions. In: Proc. Int’l. Joint Conf. Bioinformatics, Systems Biology and Intelligent Computing, Shanghai, China, pp. 3–9 (2009)

    Google Scholar 

  16. Kubat, M., Holte, R., Matwin, S.: Machine learning for the detection of oil spills in satellite radar images. Mach. Learn. 30(2-3), 195–215 (1998)

    Article  Google Scholar 

  17. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided selection. In: Proc. 14th Int’l. Conf. Machine Learning, Nashville, USA, pp. 179–186 (1997)

    Google Scholar 

  18. Lin, L., Ravitz, G., Shyu, M.L., Chen, S.C.: Effective feature space reduction with imbalanced data for semantic concept detection. In: Proc. Int’l. Conf. Sensor Networks, Ubiquitous, and Trustworthy Computing, Taichung, Taiwan, pp. 262–269 (2008)

    Google Scholar 

  19. Liu, X.Y., Zhou, Z.H.: The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proc. 6th Int’l. Conf. Data Mining, Hong Kong, pp. 970–974 (2006)

    Google Scholar 

  20. Maloof, M.: Learning when data sets are imbalanced and when costs are unequal and unknown. In: Workshop Learning from Imbalanced Data Sets II, Washington, DC (2003)

    Google Scholar 

  21. Martínez-Usó, A., Pla, F., Sotoca, J.M., García-Sevilla, P.: Clustering-based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158–4171 (2007)

    Article  Google Scholar 

  22. Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)

    Article  Google Scholar 

  23. Richards, J., Jia, X.: Using suitable neighbors to augment the training set in hyperspectral maximum likelihood classification. IEEE Geosci. Remote Sens. Lett. 5(4), 774–777 (2008)

    Article  Google Scholar 

  24. Trebar, M., Steele, N.: Application of distributed SVM architectures in classifying forest data cover types. Comput. Electron. Agr. 63(2), 119–130 (2008)

    Article  Google Scholar 

  25. Van Hulse, J., Khoshgoftaar, T., Napolitano, A., Wald, R.: Feature selection with high-dimensional imbalanced data. In: IEEE Int’l. Conf. Data Mining Workshops, Miami, USA, pp. 507–514 (2009)

    Google Scholar 

  26. Wasikowski, M., Chen, X.W.: Combating the small sample class imbalance problem using feature selection. IEEE Trans. Knowl. Data Eng. 22(10), 1388–1400 (2010)

    Article  Google Scholar 

  27. Waske, B., Benediktsson, J.A., Sveinsson, J.R.: Classifying remote sensing data with support vector machines and imbalanced training data. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 375–384. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  28. Williams, D., Myers, V., Silvious, M.: Mine classification with imbalanced data. IEEE Geosci. Remote Sens. Lett. 6(3), 528–532 (2009)

    Article  Google Scholar 

  29. Zhang, J., Mani, I.: kNN approach to unbalanced data distributions: a case study involving information extraction. In: Proc. Workshop Learning from Imbalanced Datasets, Washington DC (2003)

    Google Scholar 

  30. Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63–77 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of New Imaging Technologies Department of Computer Languages and Systems, Universitat Jaume I, Av. Sos Baynat s/n, 12071, Castelló de la Plana, Spain

    Vicente García, Javier Salvador Sánchez & Ramón A. Mollineda

Authors
  1. Vicente García
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  2. Javier Salvador Sánchez
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  3. Ramón A. Mollineda
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Editors and Affiliations

  1. Departament de Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Facultat de Matemàtiques, Gran Via de les Corts Catalanes 585, 08007, Barcelona, Spain

    Jordi Vitrià

  2. Instituto de Sistemas e Robótica / Instituto Superior Técnico, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    João Miguel Sanches

  3. Institute for Intelligent Systems and Numerical Applications in Engineering (SIANI), Edificio de Informática y Matemáticas, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017, Las Palmas, Spain

    Mario Hernández

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García, V., Sánchez, J.S., Mollineda, R.A. (2011). Classification of High Dimensional and Imbalanced Hyperspectral Imagery Data. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_80

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  • DOI: https://doi.org/10.1007/978-3-642-21257-4_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21256-7

  • Online ISBN: 978-3-642-21257-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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