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Proceedings Paper

MTI science, data products, and ground-data processing overview
Author(s): John J. Szymanski; William H. Atkins; Lee K. Balick; Christoph C. Borel; William B. Clodius; R. Wynn Christensen; Anthony B. Davis; J. Chris Echohawk; Amy E. Galbraith; Karen Lewis Hirsch; James B. Krone; Cynthia K. Little; Peter M. McLachlan; Aaron Morrison; Kimberly A. Pollock; Paul A. Pope; Curtis Novak; Keri A. Ramsey; Emily Elizabeth Riddle; Charles A. Rohde; Diane C. Roussel-Dupre; Barham W. Smith; Kathy Smith; Kim Starkovich; James P. Theiler; Paul G. Weber
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Paper Abstract

The mission of the Multispectral Thermal Imager (MTI) satellite is to demonstrate the efficacy of highly accurate multispectral imaging for passive characterization of urban and industrial areas, as well as sites of environmental interest. The satellite makes top-of-atmosphere radiance measurements that are subsequently processed into estimates of surface properties such as vegetation health, temperatures, material composition and others. The MTI satellite also provides simultaneous data for atmospheric characterization at high spatial resolution. To utilize these data the MTI science program has several coordinated components, including modeling, comprehensive ground-truth measurements, image acquisition planning, data processing and data interpretation and analysis. Algorithms have been developed to retrieve a multitude of physical quantities and these algorithms are integrated in a processing pipeline architecture that emphasizes automation, flexibility and programmability. In addition, the MTI science team has produced detailed site, system and atmospheric models to aid in system design and data analysis. This paper provides an overview of the MTI research objectives, data products and ground data processing.

Paper Details

Date Published: 20 August 2001
PDF: 9 pages
Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); doi: 10.1117/12.437009
Show Author Affiliations
John J. Szymanski, Los Alamos National Lab. (United States)
William H. Atkins, Los Alamos National Lab. (United States)
Lee K. Balick, Los Alamos National Lab. (United States)
Christoph C. Borel, Los Alamos National Lab. (United States)
William B. Clodius, Los Alamos National Lab. (United States)
R. Wynn Christensen, Los Alamos National Lab. (United States)
Anthony B. Davis, Los Alamos National Lab. (United States)
J. Chris Echohawk, Los Alamos National Lab. (United States)
Amy E. Galbraith, Los Alamos National Lab. (United States)
Karen Lewis Hirsch, Los Alamos National Lab. (United States)
James B. Krone, Los Alamos National Lab. (United States)
Cynthia K. Little, Los Alamos National Lab. (United States)
Peter M. McLachlan, Los Alamos National Lab. (United States)
Aaron Morrison, Los Alamos National Lab. (United States)
Kimberly A. Pollock, Los Alamos National Lab. (United States)
Paul A. Pope, Los Alamos National Lab. (United States)
Curtis Novak, Los Alamos National Lab. (United States)
Keri A. Ramsey, Los Alamos National Lab. (United States)
Emily Elizabeth Riddle, Los Alamos National Lab. (United States)
Charles A. Rohde, Los Alamos National Lab. (United States)
Diane C. Roussel-Dupre, Los Alamos National Lab. (United States)
Barham W. Smith, Los Alamos National Lab. (United States)
Kathy Smith, Los Alamos National Lab. (United States)
Kim Starkovich, Los Alamos National Lab. (United States)
James P. Theiler, Los Alamos National Lab. (United States)
Paul G. Weber, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 4381:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII
Sylvia S. Shen; Michael R. Descour, Editor(s)

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