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Title: imageMCR

Abstract

imageMCR is a user friendly software package that consists of a variety inputs to preprocess and analyze the hyperspectral image data using multivariate algorithms such as Multivariate Curve Resolution (MCR), Principle Component Analysis (PCA), Classical Least Squares (CLS) and Parallel Factor Analysis (PARAFAC). MCR provides a relative quantitative analysis of the hyperspectral image data without the need for standards, and it discovers all the emitting species (spectral pure components) present in an image, even those in which there is no a priori information. Once the spectral components are discovered, these spectral components can be used for future MCR analyses or used with CLS algorithms to quickly extract concentration image maps for each component within spectral image data sets.

Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
Contributing Org.:
Howland D. T. Jones Gregory A. Poulter Omar F. Garcia
OSTI Identifier:
1231484
Report Number(s):
IMAGEMCR; 002681IBMPC00
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Software
Software Revision:
00
Software Package Number:
002681
Software Package Contents:
Media Directory; Software Abstract; Media includes Source Code; User Guide; Executable Module(s); Sample Problem Input Data; Sample Problem Output Data; Installation Instructions; 1 CD-ROM
Software CPU:
IBMPC
Open Source:
No
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

. imageMCR. Computer software. Vers. 00. USDOE. 27 Sep. 2011. Web.
. (2011, September 27). imageMCR (Version 00) [Computer software].
. imageMCR. Computer software. Version 00. September 27, 2011.
@misc{osti_1231484,
title = {imageMCR, Version 00},
author = {},
abstractNote = {imageMCR is a user friendly software package that consists of a variety inputs to preprocess and analyze the hyperspectral image data using multivariate algorithms such as Multivariate Curve Resolution (MCR), Principle Component Analysis (PCA), Classical Least Squares (CLS) and Parallel Factor Analysis (PARAFAC). MCR provides a relative quantitative analysis of the hyperspectral image data without the need for standards, and it discovers all the emitting species (spectral pure components) present in an image, even those in which there is no a priori information. Once the spectral components are discovered, these spectral components can be used for future MCR analyses or used with CLS algorithms to quickly extract concentration image maps for each component within spectral image data sets.},
doi = {},
year = {2011},
month = {9},
note =
}

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