Support vectors machines regression for estimation of mars surface physical properties
نویسندگان
چکیده
In this paper, the estimation of physical properties from hyperspectral data with support vectors machines is addressed. Several kernel functions are used, from classical to advanced ones. The results are compared with Gaussian Regularized Sliced Inversion Regression and Partial Least Squares, both in terms of accuracy and complexity. Experiments on simulated data show that SVM produce highly accurate results, for some kernels, but with an increased processing time. Inversion of real images shows that SVM are robust and generalize well. In addition, the analysis of the support vectors allows to detect saturation of the physical model used to simulate data.
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