Abstract
Severely limited training data is one of the major and most common challenges in the field of hyperspectral remote sensing image classification. Supervised learning on limited training data requires either (a) designing a highly capable classifier that can handle such information scarcity, or (b) designing a highly informative and easily separable feature set. In this paper, we adapt GMM supervectors to hyperspectral remote sensing image features. We evaluate the proposed method on two datasets. In our experiments, inclusion of GMM supervectors leads to a mean classification improvement of about \(4.6\%\).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bahari, M.H., Saeidi, R., Hamme, H.V., Leeuwen, D.V.: Accent recognition using i-vector, Gaussian mean supervector and Gaussian posterior probability supervector for spontaneous telephone speech. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Institute of Electrical and Electronics Engineers. IEEE, May 2013
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Bruzzone, L., Chi, M., Marconcini, M.: A novel transductive svm for semisupervised classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 44(11), 3363–3373 (2006)
Castaings, T., Waske, B., Atli Benediktsson, J., Chanussot, J.: On the influence of feature reduction for the classification of hyperspectral images based on the extended morphological profile. Int. J. Remote Sens. 31(22), 5921–5939 (2010)
Cerva, P., Silovsky, J., Zdansky, J.: Comparison of generative and discriminative approaches for speaker recognition with limited data. Radioengineering 18(3), 307–316 (2009)
Chi, M., Feng, R., Bruzzone, L.: Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Adv. Space Res. 41(11), 1793–1799 (2008)
Christlein, V., Bernecker, D., Hönig, F., Maier, A., Angelopoulou, E.: Writer identification using GMM supervectors and exemplar-SVMs. Pattern Recogn. 63, 258–267 (2017)
Dalla Mura, M., Atli Benediktsson, J., Waske, B., Bruzzone, L.: Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int. J. Remote Sens. 31(22), 5975–5991 (2010)
Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L.: Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans. Geosci. Remote Sens. 48(10), 3747–3762 (2010)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1–38 (1977)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (2013)
Hoffbeck, J.P., Landgrebe, D.A.: Covariance matrix estimation and classification with limited training data. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 763–767 (1996)
Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Huang, X., Guan, X., Benediktsson, J.A., Zhang, L., Li, J., Plaza, A., Dalla Mura, M.: Multiple morphological profiles from multicomponent-base images for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(12), 4653–4669 (2014)
Hughes, G.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)
Jackson, Q., Landgrebe, D.A.: An adaptive classifier design for high-dimensional data analysis with a limited training data set. IEEE Trans. Geosci. Remote Sens. 39(12), 2664–2679 (2001)
Kelly, F.: Automatic recognition of ageing speakers. Ph.D. thesis, Trinity College Dublin (2014)
Kuo, B.-C., Landgrebe, D.A.: Nonparametric weighted feature extraction for classification. IEEE Trans. Geosci. Remote Sens. 42(5), 1096–1105 (2004)
Landgrebe, D.A.: Signal Theory Methods in Multispectral Remote Sensing, vol. 29. Wiley, Hoboken (2005)
Lee, C., Landgrebe, D.A.: Feature extraction based on decision boundaries. IEEE Trans. Pattern Anal. Mach. Intell. 15(4), 388–400 (1993)
Liu, T., Gu, Y., Jia, X., Benediktsson, J.A., Chanussot, J.: Class-specific sparse multiple kernel learning for spectral-spatial hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 54(12), 7351 (2016)
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Hoboken (2004)
Oliveira-Brochado, A., Martins, F.V.: Assessing the number of components in mixture models: a review. Technical report, Universidade do Porto, Faculdade de Economia do Porto (2005)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digit. Signal Proc. 10(1–3), 19–41 (2000)
Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)
Soille, P.: Constrained connectivity for hierarchical image partitioning and simplification. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1132–1145 (2008)
Srinivasan, B.V., Zotkin, D.N., Duraiswami, R.: A partial least squares framework for speaker recognition. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. Institute of Electrical and Electronics Engineers (IEEE), May 2011
Tadjudin, S., Landgrebe, D.A.: Covariance estimation for limited training samples. In: 1998 Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 1998, vol. 5, pp. 2688–2690. IEEE (1998)
Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Trans. Image Process. 22(4), 1430–1443 (2013)
Vatsavai, R.R., Shekhar, S., Burk, T.E.: A semi-supervised learning method for remote sensing data mining. In: 2005 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2005, IEEE (2005). 5 pp
Xu, M., Zhou, X., Li, Z., Dai, B., Huang, T.S.: Extended hierarchical Gaussianization for scene classification. In: 2010 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, pp. 1837–1840, September 2010
Xu, X., Li, J., Huang, X., Dalla Mura, M., Plaza, A.: Multiple morphological component analysis based decomposition for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 54(5), 3083–3102 (2016)
Zapata-Zapata, G.J., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Orozco-Arroyave, J.R.: On-line signature verification using gaussian mixture models and small-sample learning strategies. Revista Facultad de Ingeniería Universidad de Antioquia 79, 86–97 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Davari, A., Christlein, V., Vesal, S., Maier, A., Riess, C. (2017). GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-64698-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64697-8
Online ISBN: 978-3-319-64698-5
eBook Packages: Computer ScienceComputer Science (R0)