SAR and Optical data fusion based on Anisotropic Diffusion with PCA and Classification using Patch-based with LBP
This work addresses image classification in remote sensing by combining SAR and optical data, but it is incremental as it builds on existing fusion and classification methods.
The paper tackled the problem of fusing SAR and optical data for improved image classification by using anisotropic diffusion with PCA for fusion and a patch-based SVM classifier with LBP (LBP-PSVM). The results showed that LBP-PSVM outperformed SVM and PSVM classifiers for the considered data, with VV polarization performing better than VH in fusion.
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.