Digital Library of the European Council for Modelling and Simulation

Title:	Multi-Technique Data Treatment For Multi-Spectral Image Visualization
Authors:	Marina Chukalina, Andrea Somogyi, Dmitry P. Nikolaev, Gerald Schaefer
Published in:	ECMS 2008 Proceedings Edited by: Loucas S. Louca, Yiorgos Chrysanthou, Zuzana Oplatkova, Khalid Al-Begain ISBN: 978-0-9553018-6-5 Doi: 10.7148/2008 22^nd European Conference on Modelling and Simulation, Nicosia, June 3-6, 2008
Citation format:	Chukalina, M., Somogyi, A., Nikolaev, D. P., & Schaefer, G. (2008). Multi-Technique Data Treatment For Multi-Spectral Image Visualization. ECMS 2008 Proceedings edited by: L. S. Louca, Y. Chrysanthou, Z. Oplatkova, K. Al-Begain (pp. 234-237). European Council for Modeling and Simulation. doi:10.7148/2008-0234
DOI:	http://dx.doi.org/10.7148/2008-0234
Abstract:	The ‘in situ’ investigation of different biological, chemical, physical, and environmental processes often requires the knowledge of these sample characteristics with high - (sub)micro-meter - resolution as a valuable complement to the average bulk information. Different microprobe techniques can meet these requirements and their possible combinations would offer the opportunity of thorough study of a given sample or a scientific problem. Although some of the existing software packages allow for multivariate statistical treatment of data-sets obtained by a given experimental technique, none of them permits a simultaneous statistical treatment of datasets obtained by different experimental techniques, e.g. by simultaneous scanning μ-XRF (X-ray fluorescence) and μ-XRD (X-ray diffraction), or measured at several set-ups/beamlines/synchrotrons /laboratories (e.g. combined μ-IR and μ-XRF spectroscopy). The goal of this paper is to describe in detail a sequence of operations which can be applied to multi-technique datasets for obtaining a complete set of sample characteristics and to show that at present this can be realised automatically.
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