Abstract
Hyperspectral images which are captured in narrow bands in continuous manner contain very large data. This data need high processing power to classify and may contain redundant information. A variety of dimension reduction methods are used to cope with this high dimensionality. In this paper, the effect of sub-sampling hyperspectral images for dimension reduction techniques is explored and compared in classification performance and calculation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fodor, I.K.: A Survey of Dimension Reduction Techniques. UCRL-ID-148494 (May 9, 2002)
AVIRIS NW Indiana’s Indian Pines 1992 data set. (data), ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C.tif.zip (ground truth), ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/ThyFiles.zip (access Date: May 07, 2012)
Rodarmel, C., Shan, J.: Principal component analysis for hyperspectral. image classification. Surv. Land Inf. Sci. 62(2), 115–122
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, vol. 16, p. 37. The MIT Press, Cambridge (2004)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
He, X., Cai, D., Yan, S., Zhang, H.-J.: Neighborhood preserving embedding. In: Proc. ICCV (2005)
Jacob, G., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems 17 (2004)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Rosipal, R., Girolami, M.: An expectation-maximization approach to nonlinear component analysis. Neural Comput. 13(3), 505–510 (2001)
Bishop, T.: Probabilistic principal component analysis. T.Journal of the Royal Statistical Society, Series B, 61, Part 3, 611–622 (1999)
Globerson, A., Roweis, S.: Metric Learning by Collapsing Classes. In: NIPS (2005)
Weinberger, K.Q., Saul, L.K.: Unsupervised learning of image manifolds by semidefinite programming. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington DC, vol. 2, pp. 988–995 (2004)
van der Maaten, L.J.P.: An introduction to dimensionality reduction using Matlab. Technical Report 07-06, MICC-IKAT, Maastricht University, Maastricht, The Netherlands (2007)
Brand, M.: Charting a manifold. In: Advances in Neural Information Processing Systems, vol. 15, pp. 985–992. The MIT Press, Cambridge (2002)
Parasad, S., Bruce, L., Chanussot, J.: Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques (Augmented Vision and Reality) ISBN-10: 3642142117
van der Maaten, L.J.P., Postma, E.O., van den Herik, H.J.: Dimensionality Reduction: A Comparative Review, Tilburg University Technical Report, TiCC-TR 2009-005 (2009)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press Professional, Inc., San Diego (1990)
Levina, E., Bickel, P.J.: Maximum likelihood estimation of intrinsic dimension. In: Advances in Neural Information Processing Systems, vol. 17. The MIT Press, Cambridge (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Kozal, A.Ö., Teke, M., Ilgın, H.A. (2013). The Effect of Sub-sampling on Hyperspectral Dimension Reduction. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-00542-3_52
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
Online ISBN: 978-3-319-00542-3
eBook Packages: EngineeringEngineering (R0)