Observations from hyperspectral imaging sensors lead to high dimensional data sets from hundreds of images taken at closely spaced narrow spectral bands. High storage and transmission requirements, computational complexity, and statistical modeling problems combined with physical insight motivate the idea of hyperspectral dimensionality reduction using band subset selection. Many algorithms are described in the literature to solve supervised and unsupervised band subset selection problems. This paper explores the use of unsupervised band subset selection methods using column subset selection (CSS). Column subset selection is the problem (CSSP) of selecting the most independent columns of a matrix. A recent variant of this problem is the positive column subset selection problem (pCSSP) which restricts column subset selection to only consider positive linear combinations. Many algorithms have been proposed in the literature for the solution of the CSSP. However, the pCSSP is less studied. This paper will present a comparison of different algorithms to solve the CSSP and the pCSSP for band subset selection. The performance of classifiers using the algorithms as a dimensionality reduction stage will be used to evaluate the usefulness of these algorithms in hyperspectral image exploitation.
Maher Aldeghlawi and Miguel Velez-Reyes, "A comparison of column subset selection methods for hyperspectral band subset selection (Conference Presentation)," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980S (Presented at SPIE Defense + Security: April 12, 2017; Published: 7 June 2017); https://doi.org/10.1117/12.2264291.5460168976001.
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