'On the fly' dimensionality reduction for hyperspectral image acquisition

Jaime Zabalza; Jinchang Ren; Stephen Marshall

doi:10.1109/EUSIPCO.2015.7362483

'On the fly' dimensionality reduction for hyperspectral image acquisition

Jaime Zabalza, Jinchang Ren, Stephen Marshall

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

39 Downloads (Pure)

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.

Original language	English
Pages	749 - 753
Number of pages	5
DOIs	https://doi.org/10.1109/EUSIPCO.2015.7362483
Publication status	Published - 1 Sep 2015
Event	23rd European Signal Processing Conference, 2015 (EUSIPCO 2015) - Nice, France Duration: 31 Aug 2015 → 4 Sep 2015

Conference

Conference	23rd European Signal Processing Conference, 2015 (EUSIPCO 2015)
Abbreviated title	EUSIPCO 2015
Country	France
City	Nice
Period	31/08/15 → 4/09/15

Keywords

covariance matrix
principal component analysis (PCA)
hyperspectral cameras
hypercube
data reduction

Access to Document

10.1109/EUSIPCO.2015.7362483

Zabalza-etal-EUSIPCO2015-on-fly-dimensionality-reduction-hyperspectral-image-acquisitionAccepted author manuscript, 681 KB

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Cite this

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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 {\textquoteleft}on the fly{\textquoteright} during the data acquisition process. This offers great potential for HSI embedded devices to provide not only conventional HSI data but also preprocessed information.",

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