The Support Vector Machine (SVM) algorithm is assessed for the classification of polarimetric radar data for the
cartography of natural vegetation. Fully polarimetric data has been acquired in L and P bands during an AIRSAR
mission over the French Polynesian Island named Tubuai. The results show significant improvement when compared to
those obtained with the classification based on the maximum likehood criterion applied to the theoretical Wishart
distribution that are supposed a priori to be verified by radar data. Obviously, this hypothesis is not verified with the
present experimental data over the study site. The addition of other polarimetric indicators to the elements of the
polarimetric coherency matrix still improves the classification accuracy. The evaluation of different partial polarimetric
modes shows that even the best results are obtained for fully polarimetric data, the π4 mode gives the best compromise
with respect to the ASAR Alternate Polarization mode or the PALSAR Dual Polarization mode. This latter shows in turn
better results than the Alternate Polarization mode, indicating the significant contribution of the polarimetric differential
phase between 2 polarization channels.
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