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Unmixing hyperspectral data by using signal subspace sampling
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
Nagoya Univ, Inst Mat & Syst Sustainabil, Adv Measurement Technol Ctr, Chikusa Ku, Nagoya, Aichi 4648603, Japan..
Nagoya Univ, Grad Sch Engn, Chikusa Ku, Nagoya, Aichi 4648603, Japan..
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
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2017 (English)In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 182, p. 205-211Article in journal (Refereed) Published
Abstract [en]

This paper demonstrates how Signal Subspace Sampling (SSS) is an effective pre-processing step for Non-negative Matrix Factorization (NMF) or Vertex Component Analysis (VCA). The approach allows to uniquely extract non-negative source signals which are orthogonal in at least one observation channel, respectively. It is thus well suited for processing hyperspectral images from X-ray microscopy, or other emission spectroscopies, into its non-negative source components. The key idea is to resample the given data so as to satisfy better the necessity and sufficiency conditions for the subsequent NMF or VCA. Results obtained both on an artificial simulation study as well as based on experimental data from electronmicroscopy are reported. 

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 182, p. 205-211
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:uu:diva-340709DOI: 10.1016/j.ultramic.2017.07.009ISI: 000413436500026PubMedID: 28711769OAI: oai:DiVA.org:uu-340709DiVA, id: diva2:1179968
Funder
Swedish Research CouncilKnut and Alice Wallenberg Foundation, 2015.0060Available from: 2018-02-02 Created: 2018-02-02 Last updated: 2018-04-11Bibliographically approved
In thesis
1. Signal Processing Tools for Electron Microscopy
Open this publication in new window or tab >>Signal Processing Tools for Electron Microscopy
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The detection of weak signals in noisy data is a problem which occurs across various disciplines. Here, the signal of interest is the spectral signature of the electron magnetic chiral dichroism (EMCD) effect. In principle, EMCD allows for the measurement of local magnetic structures in the electron microscope, its spatial resolution, versatility and low hardware requirements giving it an eminent position among competing measurement techniques. However, experimental shortcomings as well as intrinsically low signal to noise ratio render its measurement challenging to the present day.   

This thesis explores how posterior data processing may aid the analysis of various data from the electron microscope. Following a brief introduction to different signals arising in the microscope and a yet briefer survey of the state of the art of EMCD measurements, noise removal strategies are presented. Afterwards, gears are shifted to discuss the separation of mixed signals into their physically meaningful source components based on their assumed mathematical characteristics, so called blind source separation (BSS).    

A data processing workflow for detecting weak signals in noisy spectra is derived from these considerations, ultimately culminating in several demonstrations of the extraction of EMCD signals. While the focus of the thesis does lie on data processing strategies for EMCD detection, the approaches presented here are similarly applicable in other situations. Related topics such as the general analysis of hyperspectral images using BSS methods or the fast analysis of large data sets are also discussed.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 60
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1672
National Category
Physical Sciences Computer Sciences Other Mathematics
Identifiers
urn:nbn:se:uu:diva-348264 (URN)978-91-513-0345-1 (ISBN)
Public defence
2018-06-12, Å2001, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-04-11 Last updated: 2018-05-18

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Spiegelberg, JakobPelckmans, KristiaanRusz, Jan

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