Paper: Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond
Authors: Roger N. Clark, Gregg A. Swayze, K. Eric Livo, Philip G. Brodrick, Eldar Noe Dobrea, Srinivasan Vijayarangan, Robert O. Green, David Wettergreen, Alberto Candela, Amanda Hendrix, Carlos Pérez García-Pando, Neil C. Pearson, Melissa D. Lane, Adolfo González-Romero, Xavier Querol
Figure caption: (a) Continuum removal process employed by the Tetracorder spectral feature shape-matching algorithm. Three reference spectra are shown: goethite, jarosite, and hematite. Each spectral feature has its own continuum endpoints (illustrated by the boxes). The continuum is removed from both the observed and reference spectra. For example, the hematite 0.9 μm feature continuum is removed from the cuprite unknown spectrum, then the goethite continuum is removed, and so on. This allows a specific comparison between each spectral library feature and the unknown. The spectra are offset for clarity. From R. N. Clark et al. (2003c), a public domain image. The digital spectral data for the reference minerals are included with the GitHub Tetracorder repository. (b). Spectral features as on Figure 4(a), except for comparison with vibrational absorptions near 2.2 μm. Note that alunite also has a diagnostic absorption near 1.5 μm. The spectra are offset for clarity. From R. N. Clark et al. (2003c), a public domain image. The digital spectral data for the reference minerals are included with the GitHub Tetracorder repository. (c). The continuum-removed spectra from Figure 4(a) are fit to each other using a modified least-squares calculation. The library reference feature strength is increased or decreased to best match the observed feature. Tetracorder compares the least-squares fits to many features from many library reference spectra to determine which one matches best. The solid red line in each case is the unknown, and the dashed–double-dotted black line is the library reference feature. For each feature, the least-squares correlation coefficients (the fits) are given, and along a vertical central column the weighted fits are shown. The best match to the cuprite spectrum is hematite. Hematite has two features used in the identification: the 0.9 μm feature gives a fit of 0.988, and the 0.5 μm feature gives a fit of 0.965. The area-weighted fit is 0.974. From R. N. Clark et al. (2003c), a public domain image. (d). The continuum-removed spectra from Figure 4(b) are fit together using a modified least-squares calculation. Kaolinite is the best match to the (red) cuprite spectrum. The muscovite spectrum has two features, one near 2.2 μm and the other near 2.3 μm. No 2.3 μm muscovite feature could be detected in the cuprite spectrum, so the weighted fit is zero (left hand column). Note the very similar fits between kaolinite (0.996) and halloysite (0.963), yet the halloysite profile clearly does not match as well as the kaolinite profile. This illustrates that small differences in fit numbers are significant. Alunite has two diagnostic spectral features, but the 1.5 μm feature is not shown. From R. N. Clark et al. (2003c), a public domain image.