5 research outputs found
Compositional units on Mercury from MESSENGER spectral observations: comparison of clustering techniques
The Mercury Atmospheric and Surface Composition Spectrometer (MASCS) obtained spectra of much of the surface of Mercury during the first two MESSENGER flybys of the planet [1]. The dataset have not been corrected for any effect due to observing geometry, but only converted to reflectance [2]. The characterization of spectral units is performed by statistical techniques. In order to extract the spectral shapes of the primary surface components exposed in the surface area analyzed, we applied an R-mode factor analysis [3] [4]. That leads to the evaluation of the eigenvectors of the covariance matrix and their abundances along the track. The results indicates that the NIR spectral range is carrying less information than the VIS portion and that the eigenvectors are unchanged if the full wavelength range is selected rather than limiting observations to the VIS. The analysis shows that seven eigenvectors are needed to reconstruct the original data, where each eigenvector can be regarded as a representative of a spectral class that varies in abundance along the track. The first eigenvector always displays a strong positive or “red” slope, carrying the effects associated to the viewing geometry and all eigenvectors show distinctive spectral signatures. The eigenvector abundances show marked geographical variation and a strong correlation with surface units mapped by MESSENGER’s Mercury Dual Imaging System (MDIS). We apply a decorrelation technique (Mahalanobis transformation [5]) to remove dependence on observation angle in the retrieved eigenvector abundances and then used the corrected abundances to classify or cluster the measurements and to identify spectral units. We used three clustering techniques and then we compare the output from the algorithms. At the same time, we make use of newly available high-temperature spectra from our Planetary Emissivity Laboratory [6] to assist in the identification of the components of each unit. Application to data from the first flyby provides us with confidence in the ability of these techniques to extract physical properties of surface materials
Comparison of clustering techniques for determining compositional units on Mercury from MESSENGER spectral observations.
The Mercury Atmospheric and Surface Composition Spectrometer (MASCS) obtained spectra
of much of the surface of Mercury during the rst two MESSENGER
ybys of the planet. The
resulting dataset is composed of several hundred re
ectance spectra that have not yet been
corrected for any eect due to observing geometry or to surface material phase curves. Our
hypothesis is that the separation of surface signal from other contributions can be eciently
performed by the use of statistical techniques. We adopt principal component and clustering
analyses to identify and characterize spectral units along the MASCS ground tracks. In order to
extract the spectral shapes of the primary surface components exposed in the surface area an-
alyzed, we applied an R-mode factor analysis, aiming to nd an eigenvector set that minimizes
data covariance. Identication of the dierent components and their abundances is accom-
plished by principal component analysis together with an evaluation of the eigenvectors and
eigenvalues of the covariance matrix (also called covariance matrix decomposition). A compar-
ison of the results using only the near-infrared (NIR) and visible (VIS) portions of the spectra
indicates that the NIR spectral range is carrying less information than the VIS portion. We
also nd that the eigenvectors are essentially unchanged if the full wavelength range is selected
(VIS+NIR) rather than limiting observations to the VIS range. The full-range analysis shows
that seven eigenvectors are needed to reconstruct the original spectrum to within the level of
variability associated with the observational data. Each spectral eigenvector can be regarded
as a representative of a distinct spectral class that varies in spatial abundance along the track.
The rst eigenvector always displays a strong positive or \red" slope, probably strongly linked
to uncorrected eects associated with viewing geometry variations, and all eigenvectors show
distinctive spectral signatures. Concentration coecients, or eigenvector abundances, indicate
that spectral units show marked geographical variation and a strong correlation with surface
units mapped by MESSENGER's Mercury Dual Imaging System (MDIS). Because we do not
photometrically correct the data, we apply a decorrelation technique (Mahalanobis transforma-
tion) to remove dependence on observation angle in the retrieved concentration coecients. We
obtain a set of transformed variables that no longer are eigenvector concentration coecients
or abundances. These variables therefore cannot be used to linearly reconstruct the original
dataset via inversion of the covariance matrix decomposition transformation. The Mahalanobis
decorrelation removes the variation directly linked to viewing geometry variations, but it re-
tains the eigenvector abundance variation along each track that can be used to classify the
measurements and to identify spectral units. Those coecients are analyzed through three
dierent clustering techniques: hierarchical, K-means and self-organizing maps. We evaluate
the optimal partition of our set by means of dierent validation criteria, and we compare the
output from the dierent algorithms. At the same time, we make use of newly available high-
temperature spectra from our Planetary Emissivity Laboratory to assist in the identication
of the components of each unit. Application to data from the rst
yby provides us with
condence in the ability of these techniques to extract physical properties of surface materials