Analysis of Imaging Spectrometer Data Using N-Dimensional Geometry and a Mixture-Tuned Matching Filtering Approach
Abstract
Imaging spectrometers collect unique data sets that
are simultaneously a stack of spectral images and a spectrum
for each image pixel. While these data can be analyzed using
approaches designed for multispectral images, or alternatively by
looking at individual spectra, neither of these takes full advantage
of the dimensionality of the data. Imaging spectrometer spectral
radiance data or derived apparent surface reflectance data can
be cast as a scattering of points in an n-dimensional Euclidean
space, where n is the number of spectral channels and all axes
of the n-space are mutually orthogonal. Every pixel in the data
set then has a point associated with it in the n-d space, with
its Cartesian coordinates defined by the values in each spectral
channel. Given n-dimensional data, convex and affine geometry
concepts can be used to identify the purest pixels in a given scene
(the "endmembers").N-dimensional visualization techniques permit
human interpretation of all spectral information of all image
pixels simultaneously and projection of the endmembers back to
their locations in the imagery and to their spectral signatures.
Once specific spectral endmembers are defined, partial linear
unmixing (mixture-tuned matched filtering or "MTMF" ) can be
used to spectrally unmix the data and to accurately map the
apparent abundance of a known target material in the presence of
a composite background. MTMF incorporates the best attributes
of matched filtering but extends that technique using the linear
mixed-pixel model, thus leading to high selectivity between similar
materials and minimizing classification and mapping errors for
analysis of imaging spectrometer data.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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