28 January 2002 Comparison of principal-component-based band selection methods for hyperspectral imagery
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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 motivate the idea of data reduction. A standard approach for data reduction is principal component (PC) analysis. A well-known fact for hyperspectral images (HSI) is that most of the spatial information content is summarized by the first few principal components. A disadvantage of this approach is the inherent transformation of the original HSI into linear combinations of bands with no physical relation to the spectral information content of the original image. An alternative data reduction approach is band subset selection where a subset of bands that will summarize most of the information contained in the original HSI is selected. Many approaches presented in the literature try to select bands that are a good approximation to PC because of their optimality under several criteria. This paper presents a comparison between several of these methods in terms of how well the selected bands approximate the principal components. The conditions under which good approximation of the first few principal components using a subset of bands can be achieved are presented.
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Miguel Velez-Reyes, Miguel Velez-Reyes, Daphnia M. Linares, Daphnia M. Linares, } "Comparison of principal-component-based band selection methods for hyperspectral imagery", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454170; https://doi.org/10.1117/12.454170

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