FUSION OF RADARSAT-2 AND COSMO-SKYMED POLARIMETRIC IMAGES TO IMPROVE LAND COVER CLASSIFICATION

Aim of this paper is to show how fusing SAR images having different characteristics can improve the classification accuracy, in spite of the geometrical problems rising in the fusion operation. To this end, we fused multiple-frequency (Cand X-band), multiple-polarization (HH, HV, VH and VV) and multi-resolution images. The classification has been carried out by a neural network algorithm (NN), in which the backscattering coefficients at each polarization for each image have been fused to form the input to the classifier. The evaluation of the classification accuracy has been performed in terms of overall accuracy, per class accuracy and Kappa coefficient. The obtained results show not only an enhancement of the classification accuracies, but also that more land cover classes can be better identified with respect to a single acquisition.