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Summary: 674 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 4, NO. 4, OCTOBER 2007
Feature Selection and Classification of Hyperspectral
Images With Support Vector Machines
Rick Archibald and George Fann
Abstract--Hyperspectral images consist of large number of
bands which require sophisticated analysis to extract. One ap-
proach to reduce computational cost, information representation,
and accelerate knowledge discovery is to eliminate bands that do
not add value to the classification and analysis method which is
being applied. In particular, algorithms that perform band elimi-
nation should be designed to take advantage of the structure of the
classification method used. This letter introduces an embedded-
feature-selection (EFS) algorithm that is tailored to operate
with support vector machines (SVMs) to perform band selection
and classification simultaneously. We have successfully applied
this algorithm to determine a reasonable subset of bands with-
out any user-defined stopping criteria on some sample AVIRIS
images; a problem occurs in benchmarking recursive-feature-
elimination methods for the SVMs.
Index Terms--Feature selection, hyperspectral images, support
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