Feature Selection for Anomaly Detection in Hyperspectral Data
Algorithms, Methods, and Applications
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Over the past decade, use of hyperspectral imagery has been intensively investigated for agricultural product inspection, since it introduces a new noninvasive machine-vision method that gives a very accurate inspection rate. The spectral information in hyperspectral data uniquely characterizes and identifies the chemical and/or physical properties of the constituent parts of an agricultural product that are useful for product inspection. One of the main problems in using these high-dimensional data is that there are often not enough training samples. This book, therefore, provides novel featu...
Over the past decade, use of hyperspectral imagery
has been intensively investigated for agricultural
product inspection, since it introduces a new
noninvasive machine-vision method that gives a very
accurate inspection rate. The spectral information
in hyperspectral data uniquely characterizes and
identifies the chemical and/or physical properties
of the constituent parts of an agricultural product
that are useful for product inspection. One of the
main problems in using these high-dimensional data
is that there are often not enough training samples.
This book, therefore, provides novel feature
selection algorithms to effectively reduce the
dimensionality of hyperspectral data. Experimental
results comparing the proposed algorithms to other
well-known feature selection algorithms are
presented for two case studies in chicken carcass
inspection. This book provides insightful
discussions on feature selection for hyperspectral
data for specific food safety applications and
should be especially useful to engineers and
scientists who are interested in pattern
recognition, hyperspectral data processing, food
safety research, and data mining.
has been intensively investigated for agricultural
product inspection, since it introduces a new
noninvasive machine-vision method that gives a very
accurate inspection rate. The spectral information
in hyperspectral data uniquely characterizes and
identifies the chemical and/or physical properties
of the constituent parts of an agricultural product
that are useful for product inspection. One of the
main problems in using these high-dimensional data
is that there are often not enough training samples.
This book, therefore, provides novel feature
selection algorithms to effectively reduce the
dimensionality of hyperspectral data. Experimental
results comparing the proposed algorithms to other
well-known feature selection algorithms are
presented for two case studies in chicken carcass
inspection. This book provides insightful
discussions on feature selection for hyperspectral
data for specific food safety applications and
should be especially useful to engineers and
scientists who are interested in pattern
recognition, hyperspectral data processing, food
safety research, and data mining.
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