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Proceedings Paper

Dimensionality reduction of hyperspectral imaging data using local principal components transforms
Author(s): Dimitris G. Manolakis; David B. Marden
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Paper Abstract

The spectral exploitation of hyperspectral imaging (HSI) data is based on their representation as vectors in a high dimensional space defined by a set of orthogonal coordinate axes, where each axis corresponds to one spectral band. The larger number of bands, which varies from 100-400 in existing sensors, makes the storage, transmission, and processing of HSI data a challenging task. A practical way to facilitate these tasks is to reduce the dimensionality of HSI data without significant loss of information. The purpose of this paper is twofold. First, to provide a concise review of various approaches that have been used to reduce the dimensionality of HSI data, as a preprocessing step for compression, visualization, classification, and detection applications. Second, we show that the nonlinear and nonnormal structure of HSI data, can often be more effectively exploited by using a nonlinear dimensionality reduction technique known as local principal component analyzers. The performance of the various techniques is illustrated using HYDICE and AVIRIS HSI data.

Paper Details

Date Published: 12 August 2004
PDF: 9 pages
Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); doi: 10.1117/12.542081
Show Author Affiliations
Dimitris G. Manolakis, MIT Lincoln Lab. (United States)
David B. Marden, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 5425:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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