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Title: Method to analyze remotely sensed spectral data

Abstract

A fast and rigorous multivariate curve resolution (MCR) algorithm is applied to remotely sensed spectral data. The algorithm is applicable in the solar-reflective spectral region, comprising the visible to the shortwave infrared (ranging from approximately 0.4 to 2.5 .mu.m), midwave infrared, and thermal emission spectral region, comprising the thermal infrared (ranging from approximately 8 to 15 .mu.m). For example, employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, MCR can be used to successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. Further, MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of a gas plume component near the minimum detectable quantity.

Inventors:
 [1];  [2]
  1. Albuquerque, NM
  2. Middletown, DE
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
960205
Patent Number(s):
7,491,944
Application Number:
11/410,445
Assignee:
Sandia Corporation (Albuquerque, NM)
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Stork, Christopher L, and Van Benthem, Mark H. Method to analyze remotely sensed spectral data. United States: N. p., 2009. Web.
Stork, Christopher L, & Van Benthem, Mark H. Method to analyze remotely sensed spectral data. United States.
Stork, Christopher L, and Van Benthem, Mark H. Tue . "Method to analyze remotely sensed spectral data". United States. https://www.osti.gov/servlets/purl/960205.
@article{osti_960205,
title = {Method to analyze remotely sensed spectral data},
author = {Stork, Christopher L and Van Benthem, Mark H},
abstractNote = {A fast and rigorous multivariate curve resolution (MCR) algorithm is applied to remotely sensed spectral data. The algorithm is applicable in the solar-reflective spectral region, comprising the visible to the shortwave infrared (ranging from approximately 0.4 to 2.5 .mu.m), midwave infrared, and thermal emission spectral region, comprising the thermal infrared (ranging from approximately 8 to 15 .mu.m). For example, employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, MCR can be used to successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. Further, MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of a gas plume component near the minimum detectable quantity.},
doi = {},
url = {https://www.osti.gov/biblio/960205}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2009},
month = {2}
}

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Works referenced in this record:

Spectral unmixing
journal, January 2002


Application of equality constraints on variables during alternating least squares procedures
journal, January 2002


Accounting for Poisson noise in the multivariate analysis of ToF-SIMS spectrum images
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Multivariate curve resolution for the analysis of remotely-sensed thermal infrared hyperspectral images
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  • Stork, Chris L.; Keenan, Michael R.; Haaland, David M.
  • Optical Science and Technology, the SPIE 49th Annual Meeting, SPIE Proceedings
  • https://doi.org/10.1117/12.559604

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Multivariate curve resolution applied to spectral data from multiple runs of an industrial process
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