Application of multi-output support vector regression in remore sensing inversions

This paper extends the standard support vector regression (SVR) into multi-dimensional case to estimate different biophysical parameters simultaneously. The improvement is made by the Vapnik loss function of L2 form. The proposed multi-output SVR (MO-SVR) is implemented in the joint inversion of Leaf Area Index (LAI) and vegetation cover fraction (fCover) over SPOT2/HRV1 data in the Fundulea site (VALERI), Romania. Comparison between standard SVR and MO-SVR, and the validation using biophysical maps both indicate the better fitness and accuracy of MO-SVR than the traditional form.