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2014 | OriginalPaper | Chapter

Kernel Grouped Multivariate Discriminant Analysis for Hyperspectral Image Classification

Authors : Mostafa Borhani, Hassan Ghassemian

Published in: Artificial Intelligence and Signal Processing

Publisher: Springer International Publishing

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Abstract

This paper proposes a grouping based technique of multivariate analysis, and it is extended to nonlinear kernel based version for hyperspectral image classification. Grouped multivariate analysis methods are presented in the Euclidean space and dot products are replaced by kernels in Hilbert space for nonlinear dimension reduction and data visualization. We show that the proposed kernel analysis method greatly enhances the classification performance. Experiments on Classification are presented based on Indian Pine real dataset collected from the 224-dimensional AVIRIS hyperspectral sensor, and the performance of proposed approach is investigated. Results show that the Kernel Grouped Multivariate discriminant Analysis (KGMVA) method is generally efficient to improve overall accuracy.

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Airborne Visible/Infrared Imaging Spectrometer, AVIRIS. http://aviris.jpl.nasa.gov/

Landgrebe, D.: Hyperspectral image data analysis. IEEE Signal Process. Mag. 19(1), 17–28 (2002). doi:10.1109/79.974718 CrossRef

Landgrebe, D.: On information extraction principles for hyperspectral data: a white paper. Technical report, School Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907-1285 (1997). https://engineering.purdue.edu/~landgreb/whitepaper.pdf

Yu, X., Hoff, L.E., Reed, I.S., Chen, A.M., Stotts, L.B.: Automatic target detection and recognition in multiband imagery: a unified ML detection and estimation approach. IEEE Trans. Image Process. 6(1), 143–156 (1997). doi:10.1109/83.552103 CrossRef

Schweizer, S.M., Moura, J.M.F.: Efficient detection in hyperspectral imagery. IEEE Trans. Image Process. 10(4), 584–597 (2001). doi:10.1109/83.913593 CrossRefMATH

Shaw, G., Manolakis, D.: Signal processing for hyperspectral image exploitation. IEEE Signal Process. Mag. 19(1), 12–16 (2002). doi:10.1109/79.974715 CrossRef

Hughes, G.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theor. 14(1), 55–63 (1968). doi:10.1109/TIT.1968.1054102 CrossRef

Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Annual Workshop on Computational Learning Theory, Pittsburgh, PA, pp. 144–152, (1992). doi:10.1.1.21.3818

Cortes, C., Vapnik, V.N.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). doi:10.1023/A:1022627411411 MATH

10.

Burges, C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998). doi:10.1023/A:1009715923555 CrossRef

11.

Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005). doi:10.1109/TGRS.2005.846154 CrossRef

12.

Gualtieri, J., Cromp, R.: Support vector machines for hyperspectral remote sensing classification. In: 27th AIPR Workshop Advances in Computer Assisted Recognition, Washington, DC, pp. 121–132 (1998). doi:10.1.1.27.838

13.

Brown, M., Lewis, H.G., Gunn, S.R.: Linear spectral mixture models and support vector machines for remote sensing. IEEE Trans. Geosci. Remote Sens. 38(5), 2346–2360 (2000). doi:10.1109/36.868891 CrossRef

14.

Roli, F., Fumera, G., Serpico, S.B. (ed.) Support vector machines for remote-sensing image classification. In: Proceedings of SPIE Image and Signal Processing for Remote Sensing VI, vol. 4170, pp. 160–166 (2001). doi:10.1.1.11.5830

15.

Lennon, M., Mercier, G., Hubert-Moy, L.: Classification of hyperspectral images with nonlinear filtering and support vector machines. In: IEEE International Geoscience and Remote Sensing Symposium 2002, IGARSS’02, 24–28 June 2002, vol. 3, pp. 1670–1672 (2002). doi:10.1109/IGARSS.2002.1026216

16.

Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004). doi:10.1109/TGRS.2004.831865 CrossRef

17.

Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis, 1st edn. Academic Press, New York (1980). ISBN 10: 0124712525, 13: 978-0124712522

18.

Scholokopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Technical report 44, Max Planck Institute fur biologische Kybernetik, December 1996. doi:10.1.1.29.1366

19.

Scholokopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998)CrossRef

20.

Arenas-Garcia, J., Petersen, K., Camps-Valls, G., Hansen, L.K.: Kernel multivariate analysis framework for supervised subspace learning: a tutorial on linear and kernel multivariate methods. IEEE Signal Process. Mag. 30(4), 16–29 (2013). doi:10.1109/MSP.2013.2250591 CrossRef

21.

M. Borhani, H. Ghassemian, Novel Spatial Approaches for Classification of Hyperspectral Remotely Sensed Landscapes, Symposium on Artificial Intelligence and Signal Processing, December 2013

Title: Kernel Grouped Multivariate Discriminant Analysis for Hyperspectral Image Classification
Authors: Mostafa Borhani
Hassan Ghassemian
Publisher: Springer International Publishing
Book: Artificial Intelligence and Signal Processing
Print ISBN: 978-3-319-10848-3

Electronic ISBN: 978-3-319-10849-0

Copyright Year: 2014
DOI: https://doi.org/10.1007/978-3-319-10849-0_1

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