  SAR (VV and VH polarization) and optical data are widely used in image fusion
to use the complimentary information of each other and to obtain the
better-quality image (in terms of spatial and spectral features) for the
improved classification results. This paper uses anisotropic diffusion with PCA
for the fusion of SAR and optical data and patch-based SVM Classification with
LBP (LBP-PSVM). Fusion results with VV polarization performed better than VH
polarization using considered fusion method. For classification, the
performance of LBP-PSVM using S1 (VV) with S2, S1 (VH) with S2 is compared with
SVM classifier (without patch) and PSVM classifier (with patch), respectively.
Classification results suggests that the LBP-PSVM classifier is more effective
in comparison to SVM and PSVM classifiers for considered data.
