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

TL;DR

This paper presents a novel fusion method combining anisotropic diffusion with PCA for SAR and optical data, and introduces a patch-based LBP-PSVM classifier that outperforms traditional classifiers in image classification tasks.

Contribution

The paper introduces a new fusion approach using anisotropic diffusion and PCA, and a patch-based LBP-PSVM classifier that improves classification accuracy over existing methods.

Findings

VV polarization fusion outperforms VH polarization

LBP-PSVM classifier is more effective than SVM and PSVM

Fusion enhances image quality for better classification

Abstract

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.