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Considering that Coupled Dictionary Learning (CDL) method can obtain areasonable linear mathematical relationship between resource images, we proposea novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-colorfusion method. Firstly, the traditional Brovey transform is employed as apre-processing method on the paired SAR and multispectral images. Then, CDL isused to capture the correlation between the pre-processed image pairs based onthe dictionaries generated from the source images via enforced joint sparsecoding. Afterward, the joint sparse representation in the pair of dictionariesis utilized to construct an image mask via calculating the reconstructionerrors, and therefore generate the final fusion image. The experimentalverification results of the SAR images from the Sentinel-1 satellite and themultispectral images from the Landsat-8 satellite show that the proposed methodcan achieve superior visual effects, and excellent quantitative performance interms of spectral distortion, correlation coefficient, MSE, NIQE, BRISQUE, andPIQE.

Joint Sparse Representations and Coupled Dictionary Learning in Multi-Source Heterogeneous Image Pseudo-color Fusion