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2019 | OriginalPaper | Chapter

Band Selection with Bhattacharyya Distance Based on the Gaussian Mixture Model for Hyperspectral Image Classification

Authors : Mohammed Lahlimi, Mounir Ait Kerroum, Youssef Fakhri

Published in: Recent Advances in Electrical and Information Technologies for Sustainable Development

Publisher: Springer International Publishing

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Abstract

This paper investigates a new band selection approach with the Bhattacharyya distance based on the Gaussian Mixture Model (GMM) for Hyperspectral image classification. Our main motivation to model the Bhattacharyya distance using GMM is due to the fact that this tool is well known for capturing non-Gaussian statistic of multivariate data and that is less sensitive to estimation error problem than purely non-parametric models. To estimate the parameters of GMM, a Robust Expectation-Maximization (REM) algorithm is used. REM solves the shortcoming of the classical Expectation-Maximization (EM) algorithm by dynamically adapting the number of clusters to the data structure. The selected bands with the proposed approach are compared, in terms of classification accuracy, to the Bhattacharyya expressed in its parametric form and the Bhattacharyya modelled with GMM using the classical EM algorithm. The experiment was carried out on two real hyperspectral images, the Indiana Pines (92AV3C) sub-scene and the Kennedy Space Center (KSC) dataset, and the experimental results have demonstrated the effectiveness of our proposed method in terms of classification accuracy with fewer bands.

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AitKerroum, M., Hammouch, A., & Aboutajdine, D. (2010). Textural feature selection by joint mutual information based on gaussian mixture model for multispectral image classification. Pattern Recognition Letters, 31(10), 1168–1174. https://doi.org/10.1016/j.patrec.2009.11.010.CrossRef

Baumgardner, M. F., Biehl, L. L., & Landgrebe, D. A. (2015). 220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3. https://doi.org/10.4231/r7rx991c, https://purr.purdue.edu/publications/1947/1.

Burrell, L., Smart, O., Georgoulas, G. K., Marsh, E., & Vachtsevanos, G. J. (2007). Evaluation of feature selection techniques for analysis of functional MRI and EEG. In DMIN (pp. 256–262).

Camps-Valls, G., & Bruzzone, L. (2009). Kernel methods for remote sensing data analysis. Wiley.

Datta, A., Ghosh, S., & Ghosh, A. (2014). Band elimination of hyperspectral imagery using partitioned band image correlation and capacitory discrimination. International Journal of Remote Sensing, 35(2), 554–577. https://doi.org/10.1080/01431161.2013.871392.CrossRef

Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed.). New York, NY, USA: Wiley-Interscience.MATH

Dundar, M. M., & Landgrebe, D. (2002). A model-based mixture-supervised classification approach in hyperspectral data analysis. IEEE Transactions on Geoscience and Remote Sensing, 40(12), 2692–2699. https://doi.org/10.1109/TGRS.2002.807010.CrossRef

Dundar, M. M., & Landgrebe, D. A. (2004). Toward an optimal supervised classifier for the analysis of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 42(1), 271–277. https://doi.org/10.1109/TGRS.2003.817813.CrossRef

GIC UdPV. (2015). Hyperspectral remote sensing scenes—kennedy space center (KSC). http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes#Kennedy_Space_Center.28KSC.29.

Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 985–990). https://doi.org/10.1109/ijcnn.2004.1380068.

Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126.CrossRef

Jimenez, L. O., & Landgrebe, D. A. (1998). Supervised classification in high-dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(1), 39–54. https://doi.org/10.1109/5326.661089.

Kuo, B. C., & Landgrebe, D. A. (2002). A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2486–2494. https://doi.org/10.1109/TGRS.2002.805088.CrossRef

Le Bris, A., Chehata, N., Briottet, X., & Paparoditis, N. (2015). Extraction of optimal spectral bands using hierarchical band merging out of hyperspectral data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(3), 459.CrossRef

Lee, C., & Landgrebe, D. A. (1993). Feature extraction based on decision boundaries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4), 388–400. https://doi.org/10.1109/34.206958.CrossRef

Li, W., Prasad, S., & Fowler, J. E. (2014). Hyperspectral image classification using gaussian mixture models and markov random fields. IEEE Geoscience and Remote Sensing Letters, 11(1), 153–157. https://doi.org/10.1109/LGRS.2013.2250905.CrossRef

Martinez, W., & Martinez, A. (2007). Computational Statistics Handbook with MATLAB (2nd ed.). Chapman & Hall/CRC Computer Science & Data Analysis: CRC Press.MATH

Richards, J. (2012). Remote sensing digital image analysis: An introduction. Berlin: Springer. https://books.google.com/books?id=ETfwQnBMP4UC.

Shahshahani, B. M., & Landgrebe, D. A. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 32(5), 1087–1095. https://doi.org/10.1109/36.312897.CrossRef

Simin, C., Rongqun, Z., Wenling, C., & Hui, Y. (2009). Band selection of hyperspectral images based on bhattacharyya distance. WSEAS Transactions on Information Science and Applications, 6(7), 1165–1175.

Tadjudin, S., & Landgrebe, D. A. (2000). Robust parameter estimation for mixture model. IEEE Transactions on Geoscience and Remote Sensing, 38(1), 439–445. https://doi.org/10.1109/36.823939.CrossRef

Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (2nd ed.). Elsevier Science.

Thomaz, C. E., Gillies, D. F., & Feitosa, R. Q. (2004). A new covariance estimate for bayesian classifiers in biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(2), 214–223. https://doi.org/10.1109/TCSVT.2003.821984.CrossRef

Wang, S., & Wang, C. (2015). Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 159.CrossRef

Webb, A. (2003). Statistical pattern recognition (2nd ed.). Wiley InterScience Electronic Collection, Wiley.

Yang, L., & Lin Yang, M. S., Lai, C. Y., & Lin, C. Y. (2012). A robust EM clustering algorithm for Gaussian mixture models. Pattern Recognition, 45(11), 3950–3961, https://doi.org/10.1016/j.patcog.2012.04.031.

Title: Band Selection with Bhattacharyya Distance Based on the Gaussian Mixture Model for Hyperspectral Image Classification
Authors: Mohammed Lahlimi
Mounir Ait Kerroum
Youssef Fakhri
Publisher: Springer International Publishing
Book: Recent Advances in Electrical and Information Technologies for Sustainable Development
Print ISBN: 978-3-030-05275-1

Electronic ISBN: 978-3-030-05276-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-05276-8_10

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