Estimation of Mars surface physical properties from hyperspectral images using the SIR method
2 : MISTIS
(INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
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Laboratoire Jean Kuntzmann, INRIA
We propose a statistical method to evaluate the physical properties of surface materials on Mars from hyperspectral images collected by the OMEGA instrument aboard the Mars express spacecraft. The approach we develop is based on the estimation of the functional relationship f between some physical parameters and observed spectra. For this purpose, a database of synthetic spectra is generated by a physical radiative transfer model and used to estimate f. The high dimension of spectra is reduced by using a regularized version of Sliced Inverse Regression (SIR) to overcome the curse of dimensionality and consequently the sensitivity of the inversion to noise (ill-conditioned problem). Compared with a naive spectrum matching approach such as the k-nearest neighbors algorithm, estimates are more accurate and realistic.
References:
C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes & S. Girard. "Retrieval of Mars surface physical properties from OMEGA hyperspectral images using Regularized Sliced Inverse Regression", Journal of Geophysical Research - Planets, 114, E06005, 2009.
C. Bernard-Michel, L. Gardes & S. Girard. "Gaussian Regularized Sliced Inverse Regression", Statistics and Computing, 19, 85--98, 2009.
References:
C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes & S. Girard. "Retrieval of Mars surface physical properties from OMEGA hyperspectral images using Regularized Sliced Inverse Regression", Journal of Geophysical Research - Planets, 114, E06005, 2009.
C. Bernard-Michel, L. Gardes & S. Girard. "Gaussian Regularized Sliced Inverse Regression", Statistics and Computing, 19, 85--98, 2009.
