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

SAR and Optical data fusion based on Anisotropic Diffusion with PCA and Classification using Patch-based with LBP