Abstract
Hyperspectral imaging (HSI) combines spectroscopy and imaging resulting in three dimensional multivariate data structures (‘hypercubes’). Each pixel in a hypercube contains a spectrum representing its light absorbing and scattering properties. This spectrum can be used to estimate chemical composition and/or physical properties of the spatial region represented by that pixel. One of the advantages of HSI is the large volume of data available in each hypercube with which to create calibration and training sets. This is also known as the curse of dimensionality, due to the resultant high computational load of high dimensional data. It is desirable to decrease the computational burden implied in hyperspectral imaging; this is especially relevant in the development of real time applications. This paper gives an overview of some pertinent issues for the handling of HSI data. Computational considerations involved in acquiring and managing HSI data are discussed and an overview of the multivariate analysis methods available for reducing the considerable data load encountered in HSI data is presented.
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