Machine learning techniques for the inversion of planetary hyperspectral images

Caroline Bernard-Michel 1 Sylvain Douté 2 Mathieu Fauvel 1 Laurent Gardes 1 Stephane Girard 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper, the physical analysis of planetary hyperspectral images is addressed. To deal with high dimensional spaces (image cubes present 256 bands), two methods are proposed. The first method is the support vectors machines regression (SVM-R) which applies the structural risk minimization to perform a non-linear regression. Several kernels are investigated in this work. The second method is the Gaussian regularized sliced inverse regression (GRSIR). It is a two step strategy; the data are map onto a lower dimensional vector space where the regression is performed. Experimental results on simulated data sets have showed that the SVM-R is the most accurate method. However, when dealing with real data sets, the GRSIR gives the most interpretable results.
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Communication dans un congrès
WHISPERS '09 - 1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Aug 2009, Grenoble, France. IEEE, pp.1-4, 2009, 〈10.1109/WHISPERS.2009.5289010〉
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Caroline Bernard-Michel, Sylvain Douté, Mathieu Fauvel, Laurent Gardes, Stephane Girard. Machine learning techniques for the inversion of planetary hyperspectral images. WHISPERS '09 - 1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Aug 2009, Grenoble, France. IEEE, pp.1-4, 2009, 〈10.1109/WHISPERS.2009.5289010〉. 〈hal-00761720〉

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