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Multivariate analysis of hyperspectral hard X‐ray images
Author(s) -
Egan Christopher K.,
Jacques Simon D. M.,
Cernik Robert J.
Publication year - 2013
Publication title -
x‐ray spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
eISSN - 1097-4539
pISSN - 0049-8246
DOI - 10.1002/xrs.2448
Subject(s) - hyperspectral imaging , multivariate statistics , pixel , computer science , imaging spectrometer , focus (optics) , multivariate analysis , sample (material) , exploit , artificial intelligence , pattern recognition (psychology) , remote sensing , spectrometer , optics , chemistry , physics , geology , machine learning , computer security , chromatography
This article describes methods to analyse and process hyperspectral hard X‐ray imaging data. We focus on the use of multivariate techniques that exploit the spectral information to make informed decisions on the material content within each pixel of an X‐ray image. These analysis methods have the ability to auto‐segment data without prior knowledge of the sample composition or structure, and are particularly useful for studying completely unknown, diluted or complex specimens. We demonstrate the methods on a variety of hard X‐ray images including X‐ray fluorescence and absorption data recorded using a hard X‐ray imaging spectrometer. The multivariate methods described are very powerful with the ability to segment, distinguish and, in some cases, identify different materials within a single X‐ray image. Potential uses of hyperspectral X‐ray imaging are discussed varying from materials science to industrial or security applications. Copyright © 2013 John Wiley & Sons, Ltd.