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Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical
chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the
landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods
are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and
mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA
(Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing
Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey
(USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear
regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being
analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed
MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF
was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the
country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil
contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in
oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach
to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.
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