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Computational Intelligence in Image Processing pp 265–280Cite as

Multiobjective PSO for Hyperspectral Image Clustering

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Abstract

In this chapter, a multiobjective particle-swarm optimization approach is presented as an answer to the problem of hyperspectral remote sensing image clustering. It aims at simultaneously solving the following three different issues: (1) clustering the hyperspectral cube under analysis; (2) detecting the most discriminative bands of the hypercube; (3) avoiding the user to set a priori the number of data classes. The search process is guided by three different statistical criteria, which are the log-likelihood function, the Bhattacharyya distance, and the minimum description length. Experimental results clearly underline the effectiveness of particle-swarm optimizers for a completely automatic and unsupervised analysis of hyperspectral remote sensing images.

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References

  1. Bandyopadhyay, S., Maulik, U., Mukhopadhyay, A.: Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 45(5), 1506–1511 (2007)

    Article  Google Scholar 

  2. Barni, H., Garzelli, A., Mecocci, A., Sabatini, L.: A robust fuzzy clustering algorithm for the classification of remote sensing images. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2000, Honolulu, Hawaii, vol. 5, pp. 2143–2145 (2000)

    Google Scholar 

  3. Bazi, Y., Melgani, F.: Semisupervised PSO-SVM regression for biophysical parameter estimation. IEEE Trans. Geosci. Remote Sens. 45(6), 1887–1895 (2007)

    Article  Google Scholar 

  4. Berge, A., Solberg, A.: Structured Gaussian components for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 44(11), 3386–3396 (2006)

    Article  Google Scholar 

  5. Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math. Soc. 35, 99–109 (1943)

    MathSciNet  MATH  Google Scholar 

  6. Bilgin, G., Erturk, S., Yildirim, T.: Unsupervised classification of hyperspectral-image data using fuzzy approaches that spatially exploit membership relations. IEEE Geosci. Remote Sens. Lett. 5(4), 673–677 (2008)

    Article  Google Scholar 

  7. Broilo, M., De Natale, F.: A stochastic approach to image retrieval using relevance feedback and particle-swarm optimization. IEEE Trans. Multimedia 12(4), 267–277 (2010)

    Article  Google Scholar 

  8. Clerc, M.: Particle-Swarm Optimization. ISTE, London (2006)

    Book  MATH  Google Scholar 

  9. Deb, K.: MultiObjective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    Google Scholar 

  10. Duda, R., Hart, P., Stork, D.: Pattern Classification, vol. 2. Wiley, New York (2001)

    Google Scholar 

  11. Dundar, M., Landgrebe, D.: A model-based mixture-supervised classification approach in hyperspectral data analysis. IEEE Trans. Geosci. Remote Sens. 40(12), 2692–2699 (2002)

    Article  Google Scholar 

  12. Eberhart, R., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)

    Google Scholar 

  13. Frery, A., de Araujo, C., Alice, H., Cerqueira, J., Loureiro, J., de Lima, M., Oliveira, M., Horta, M.: Hyperspectral images clustering on reconfigurable hardware using the k-means algorithm. In: Proceedings of the IEEE Symposium on Integrated Circuits and Systems Design 2003, Sao Paulo, Brazil, pp. 99–104. (2003)

    Google Scholar 

  14. Gaing, Z.: A particle-swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004)

    Article  Google Scholar 

  15. Jia, X., Richards, J.: Cluster-space representation for hyperspectral data classification. IEEE Trans. Geosci. Remote Sens. 40(3), 593–598 (2002)

    Article  Google Scholar 

  16. Jia, X., Richards, J.: Efficient transmission and classification of hyperspectral image data. IEEE Trans. Geosci. Remote Sens. 41(5), 1129–1131 (2003)

    Article  Google Scholar 

  17. Kersten, P., Lee, J., Ainsworth, T.: Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering. IEEE Trans. Geosci. Remote Sens. 43(3), 519–527 (2005)

    Article  Google Scholar 

  18. Lee, S., Crawford, M.: Hierarchical clustering approach for unsupervised image classification of hyperspectral data. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2004, vol. 2, pp. 941–944. Anchorage, Alaska (2004)

    Google Scholar 

  19. Liang, Z., Jaszczak, R., Coleman, R.: Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing. IEEE Trans. Nucl. Sci. 39(4), 1126–1133 (1992)

    Article  Google Scholar 

  20. Liu, X., Li, X., Zhang, Y., Yang, C., Xu, W., Li, M., Luo, H.: Remote sensing image classification based on dot density function weighted FCM clustering algorithm. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2007, vol. 2, pp. 2010–2013. Barcelona, Spain (2007)

    Google Scholar 

  21. Lizzi, L., Viani, F., Azaro, R., Massa, A.: A PSO-driven spline-based shaping approach for ultrawideband (UWB) antenna synthesis. IEEE Trans. Antennas Propag. 56(8), 2613–2621 (2008)

    Article  Google Scholar 

  22. Marçal, A., Castro, L.: Hierarchical clustering of multispectral images using combined spectral and spatial criteria. IEEE Geosci. Remote Sens. Lett. 2(1), 59–63 (2005)

    Article  Google Scholar 

  23. McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2000)

    Book  MATH  Google Scholar 

  24. Melgani, F., Bazi, Y.: Classification of electrocardiogram signals with support vector machines and particle-swarm optimization. IEEE Trans. Inf. Technol. Biomed. 12(5), 667–677 (2008)

    Article  Google Scholar 

  25. Palubinskas, G., Datcu, M., Pac, R.: Clustering algorithms for large sets of heterogeneous remote sensing data. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 1999, vol. 3, pp. 1591–1593. Hamburg, Germany (1999)

    Google Scholar 

  26. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(6), 559–572 (1901)

    Google Scholar 

  27. Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific, Singapore (1989)

    MATH  Google Scholar 

  28. Shi, H., Shen, Y., Liu, Z.: Hyperspectral bands reduction based on rough sets and fuzzy C-means clustering. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference 2003, vol. 2, pp. 1053–1056. Vail, Colorado (2003)

    Google Scholar 

  29. Slade, W., Ressom, H., Musavi, M., Miller, R.: Inversion of ocean color observations using particle-swarm optimization. IEEE Trans. Geosci. Remote Sens. 42(9), 1915–1923 (2004)

    Article  Google Scholar 

  30. Tran, T., Wehrens, R., Hoekman, D., Buydens, L.: Initialization of Markov random field clustering of large remote sensing images. IEEE Trans. Geosci. Remote Sens. 43(8), 1912–1919 (2005)

    Article  Google Scholar 

  31. Tyagi, M., Bovolo, F., Mehra, A., Chaudhuri, S., Bruzzone, L.: A context-sensitive clustering technique based on graph-cut initialization and expectation-maximization algorithm. IEEE Geosci. Remote Sens. Lett. 5(1), 21–25 (2008)

    Article  Google Scholar 

  32. Webb, A.: Statistical Pattern Recognition. Wiley, Chichester (2002)

    Book  MATH  Google Scholar 

  33. Wong, Y., Posner, E.: A new clustering algorithm applicable to multispectral and polarimetric SAR images. IEEE Trans. Geosci. Remote Sens. 31(3), 634–644 (1993)

    Article  Google Scholar 

  34. Xia, G., He, C., Sun, H.: A rapid and automatic MRF-based clustering method for SAR images. IEEE Geosci. Remote Sens. Lett. 4(4), 596–600 (2007)

    Article  Google Scholar 

  35. Zhong, Y., Zhang, L., Huang, B., Li, P.: An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 44(2), 420–431 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 14, 38123, Trento, Italy

    Farid Melgani & Edoardo Pasolli

Authors
  1. Farid Melgani
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  2. Edoardo Pasolli
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Correspondence to Farid Melgani .

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Editors and Affiliations

  1. , Electrical Engineering Department, Jadavpur University, Kolkata, 700032, West Bengal, India

    Amitava Chatterjee

  2. , LISSI, University of Paris-Est Créteil, 61 avenue du Général de Gaulle, Créteil, 94010, France

    Patrick Siarry

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Melgani, F., Pasolli, E. (2013). Multiobjective PSO for Hyperspectral Image Clustering. In: Chatterjee, A., Siarry, P. (eds) Computational Intelligence in Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30621-1_14

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  • DOI: https://doi.org/10.1007/978-3-642-30621-1_14

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  • Print ISBN: 978-3-642-30620-4

  • Online ISBN: 978-3-642-30621-1

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