Compression of first-order spectral data using the B-spline zero compression method

Olsson, Ralf J. O.; Karlsson, Mikael; Moberg, Ludvig

doi:10.1002/(sici)1099-128x(199609)10:5/6<399::aid-cem440>3.0.co;2-i

Cited by 5 publications

(3 citation statements)

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“…Spectroscopists and analytical chemists have employed compression in the spectral domain to accelerate and improve data analysis. [1][2][3][4][5][6] The technique of variable selection is important for a 1 Sandia is a multiprogram laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. *mhvanbe@sandia.gov; phone 1-505-844-5443; www.sandia.gov †dpwoodb@sandia.gov variety of reasons.…”

Section: Spectral Compressionmentioning

confidence: 99%

An evaluation of algorithms and methods for compressing and decompressing atmospheric transmission data for use in at-sensor measurements

Benthem

Woodbury

2015

SPIE Proceedings

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In this paper, we describe the use of various methods of one-dimensional spectral compression by variable selection as well as principal component analysis (PCA) for compressing multi-dimensional sets of spectral data. We have examined methods of variable selection such as wavelength spacing, spectral derivatives, and spectral integration error. After variable selection, reduced transmission spectra must be decompressed for use. Here we examine various methods of interpolation, e.g., linear, cubic spline and piecewise cubic Hermite interpolating polynomial (PCHIP) to recover the spectra prior to estimating at-sensor radiance. Finally, we compressed multi-dimensional sets of spectral transmittance data from moderate resolution atmospheric transmission (MODTRAN) data using PCA. PCA seeks to find a set of basis spectra (vectors) that model the variance of a data matrix in a linear additive sense. Although MODTRAN data are intricate and are used in nonlinear modeling, their base spectra can be reasonably modeled using PCA yielding excellent results in terms of spectral reconstruction and estimation of at-sensor radiance. The major finding of this work is that PCA can be implemented to compress MODTRAN data with great effect, reducing file size, access time and computational burden while producing high-quality transmission spectra for a given set of input conditions.

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Section: Spectral Compressionmentioning

confidence: 99%

An evaluation of algorithms and methods for compressing and decompressing atmospheric transmission data for use in at-sensor measurements

Benthem

Woodbury

2015

SPIE Proceedings

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“…Computational details on B-splines can be found in de Boor (1978) or in e.g. Alsberg and Kvalheim (1993); Olsson et al (1996).…”

Section: Spline Approximationmentioning

confidence: 99%

“…of order 1), which corresponds to piecewise constant approximation, has been used for compression purpose, see, e.g., Alsberg and Kvalheim (1993); ; Alsberg and Kvalheim (1994); Olsson et al (1996). Those papers take advantage of the linear relationship between a spectrum and its coordinates on the Bspline basis.…”

Section: B-spline Coordinatesmentioning

confidence: 99%

Fast selection of spectral variables with B-spline compression

Rossi

François

Wertz

et al. 2007

Chemometrics and Intelligent Laboratory Systems

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The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of the selected variables. Since the optimal approach of testing all possible subsets of variables with the prediction model is intractable, an incremental selection approach using a nonparametric statistics is a good option, as it avoids the computationally intensive use of the model itself. It has two drawbacks however: the number of groups of variables to test is still huge, and colinearities can make the results unstable. To overcome these limitations, this paper presents a method to select groups of spectral variables. It consists in a forward-backward procedure applied to the coefficients of a B-Spline representation of the spectra. The criterion used in the forward-backward procedure is the mutual information, allowing to find nonlinear dependencies between variables, on the contrary of the generally used correlation. The spline representation is used to get interpretability of the results, as groups of consecutive spectral variables will be selected. The experiments conducted on NIR spectra from fescue grass and diesel fuels show that the method provides clearly identified groups of selected variables, making interpretation easy, while keeping a low computational load. The prediction performances obtained using the selected coefficients are higher than those obtained by the same method applied directly to the original variables and similar to those obtained using traditional models, although using significantly less spectral variables.

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