Analysis of electron energy loss spectroscopy data using geometric extraction methods

Jakob Spiegelberg; Ján Rusz; Thomas Thersleff; Kristiaan Pelckmans

doi:10.1016/j.ultramic.2016.12.014

Analysis of electron energy loss spectroscopy data using geometric extraction methods

Ultramicroscopy. 2017 Mar:174:14-26. doi: 10.1016/j.ultramic.2016.12.014. Epub 2016 Dec 16.

Authors

Jakob Spiegelberg¹, Ján Rusz², Thomas Thersleff³, Kristiaan Pelckmans⁴

Affiliations

¹ Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden. Electronic address: jakob.spiegelberg@physics.uu.se.
² Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.
³ Department of Engineering Sciences, Uppsala University, Box 534, S-751 21 Uppsala, Sweden.
⁴ Department of Information Technology, Uppsala University, Box 337, S-751 05 Uppsala, Sweden.

PMID: 28012371
DOI: 10.1016/j.ultramic.2016.12.014

Abstract

A set of geometric data decomposition methods is discussed. In particular, randomized vertex component analysis (RVCA), an extension of vertex component analysis (VCA) for the application to noisy data, is established. Minimum volume simplex analysis (MVSA), a recent technique for the extraction of endmembers in the absence of pure pixels, is presented. A comparison between MVSA and the previously presented technique of Bayesian Linear Unmixing (BLU) is drawn. Lastly, the efficiency of these methods for high-dimensional data is examined. Improvement on the extracted source components spectral signatures are achieved by establishing Gaussian mixture modeling as extraction technique.

Keywords: Blind source separation; Data clustering; EELS; Geometric extraction methods; RVCA.

Publication types

Research Support, Non-U.S. Gov't