Abstract
Dimensionality Reduction (DR) has found many applications in hyperspectral image processing. This book chapter investigates Projection Pursuit (PP)-based Dimensionality Reduction, (PP-DR) which includes both Principal Components Analysis (PCA) and Independent Component Analysis (ICA) as special cases. Three approaches are developed for PP-DR. One is to use a Projection Index (PI) to produce projection vectors to generate Projection Index Components (PICs). Since PP generally uses random initial conditions to produce PICs, when the same PP is performed in different times or by different users at the same time, the resulting PICs are generally different in terms of components and appearing orders. To resolve this issue, a second approach is called PI-based PRioritized PP (PI-PRPP) which uses a PI as a criterion to prioritize PICs. A third approach proposed as an alternative to PI-PRPP is called Initialization-Driven PP (ID-PIPP) which specifies an appropriate set of initial conditions that allows PP to produce the same PICs as well as in the same order regardless of how PP is run. As shown by experimental results, the three PP-DR techniques can perform not only DR but also separate various targets in different PICs so as to achieve unsupervised target detection.
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
References
Wang, J. and Chang, C.-I, “Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis,” IEEE Trans. on Geoscience and Remote Sensing, 44(6), 1586–1600 (2006).
Hyvarinen, A., Karhunen, J. and Oja, E., [Independent Component Analysis], John Wiley & Sons (2001).
Ren, H., Du, Q., Wang, J., Chang, C.-I and Jensen, J., “Automatic target recognition hyperspectral imagery using high order statistics,” IEEE Trans. on Aerospace and Electronic Systems, 1372–1385 (2006).
Duda, R. O. and Hart, P. E., Pattern Classification and Scene analysis, John Wiley & Sons, 1973.
Friedman, J. H., and Tukey, J. W., “A projection pursuit algorithm for exploratory data analysis,” IEEE Transactions on Computers, c-23(9), 881–889 (1974).
Chang, C.-I, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers, New York (2003).
Ren, H., Du, Q., Wang, J., Chang, C.-I and Jensen, J., “Automatic traget recognition hyperspectral imagery using high order statistics,” IEEE Trans. on Aerospace and Electronic Systems, 42(4), 1372–1385 (2006).
Chu, S., Ren, H. and Chang, C.-I, “High order statistics-based approaches to endmember extraction for hyperspectral imagery,” to be presented in SPIE 6966, (2008).
Hyvarinen, A. and Oja, E., “A fast fixed-point for independent component analysis,” Neural Comp., 9(7), 1483–1492 (1997).
Ren, H. and Chang, C.-I, “Automatic spectral target recognition in hyperspectral imagery,” IEEE Trans. on Aerospace and Electronic Sys., 39(4), 1232–1249 (2003).
Harsanyi, J. and Chang, C.-I, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach,” IEEE Trans. on Geoscience and Remote Sensing, 32(4), 779–785 (1994).
Chang, C.-I, and Du, Q., “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 3, pp. 608–619, March 2004.
Winter, M.E., “N-finder: an algorithm for fast autonomous spectral endmember determination in hyperspectral data,” Image Spectrometry V, Proc. SPIE 3753, pp. 266–277, 1999.
Chang, Y.-C., Ren, H., Chang, C.-I and Rand, B., “How to design synthetic images to validate and evaluate hyperspectral imaging algorithms,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16–20, Orlando, Florida, 2008.
Safavi, H., and Chang, C.-I , “Projection pursuit-based dimensionality reduction,” SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16–20, Orlando, Florida, 2008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Safavi, H., Chang, CI., Plaza, A.J. (2012). Projection Pursuit-Based Dimensionality Reduction for Hyperspectral Analysis. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_14
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1183-3_14
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-1182-6
Online ISBN: 978-1-4614-1183-3
eBook Packages: EngineeringEngineering (R0)