'On the fly' dimensionality reduction for hyperspectral image acquisition

Research output: Contribution to conferencePaper

1 Citation (Scopus)
35 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 languageEnglish
Pages749 - 753
Number of pages5
DOIs
Publication statusPublished - 1 Sep 2015
Event23rd European Signal Processing Conference, 2015 (EUSIPCO 2015) - Nice, France
Duration: 31 Aug 20154 Sep 2015

Conference

Conference23rd European Signal Processing Conference, 2015 (EUSIPCO 2015)
Abbreviated titleEUSIPCO 2015
CountryFrance
CityNice
Period31/08/154/09/15

Keywords

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

Fingerprint Dive into the research topics of ''On the fly' dimensionality reduction for hyperspectral image acquisition'. Together they form a unique fingerprint.

  • Cite this

    Zabalza, J., Ren, J., & Marshall, S. (2015). 'On the fly' dimensionality reduction for hyperspectral image acquisition. 749 - 753. Paper presented at 23rd European Signal Processing Conference, 2015 (EUSIPCO 2015), Nice, France. https://doi.org/10.1109/EUSIPCO.2015.7362483