Dimensionality reduction of hyperspectral imaging data using local principal components transforms

Dimitris G. Manolakis; David B. Marden

doi:10.1117/12.542081

12 August 2004 Dimensionality reduction of hyperspectral imaging data using local principal components transforms

Dimitris G. Manolakis, David B. Marden

Proceedings Volume 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X; (2004) https://doi.org/10.1117/12.542081
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

Dimitris G. Manolakis and David B. Marden "Dimensionality reduction of hyperspectral imaging data using local principal components transforms", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); https://doi.org/10.1117/12.542081

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