Projection Pursuit-Based Dimensionality Reduction for Hyperspectral Analysis

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. H. Safavi (*) Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA e-mail: [email protected] C.-I Chang Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan e-mail: [email protected] A.J. Plaza Department of Technology of Computers and Communications, University of Extremadura, Escuela Politecnica de Caceres, Caceres, SPAIN e-mail: [email protected] B. Huang (ed.), Satellite Data Compression, DOI 10.1007/978-1-4614-1183-3_14, # Springer Science+Business Media, LLC 2011 287