'On the fly' dimensionality reduction for hyperspectral image acquisition

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Zabalza, Jaime and Ren, Jinchang and Marshall, Stephen (2015) 'On the fly' dimensionality reduction for hyperspectral image acquisition. In: 23rd European Signal Processing Conference, 2015 (EUSIPCO 2015), 2015-08-31 - 2015-09-04. (https://doi.org/10.1109/EUSIPCO.2015.7362483)

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Abstract

Hyperspectral imaging (HSI) devices produce 3-D hyper-cubes of a spatial scene in hundreds of different spectral bands, generating large data sets which allow accurate data processing to be implemented. However, the large dimen-sionality of hypercubes leads to subsequent implementation of dimensionality reduction techniques such as principal component analysis (PCA), where the covariance matrix is constructed in order to perform such analysis. In this paper, we describe how the covariance matrix of an HSI hyper-cube can be computed in real time ‘on the fly’ during the data acquisition process. This offers great potential for HSI embedded devices to provide not only conventional HSI data but also preprocessed information.

ORCID iDs

Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194 and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
CORE (COnnecting REpositories)