  Considering that Coupled Dictionary Learning (CDL) method can obtain a
reasonable linear mathematical relationship between resource images, we propose
a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color
fusion method. Firstly, the traditional Brovey transform is employed as a
pre-processing method on the paired SAR and multispectral images. Then, CDL is
used to capture the correlation between the pre-processed image pairs based on
the dictionaries generated from the source images via enforced joint sparse
coding. Afterward, the joint sparse representation in the pair of dictionaries
is utilized to construct an image mask via calculating the reconstruction
errors, and therefore generate the final fusion image. The experimental
verification results of the SAR images from the Sentinel-1 satellite and the
multispectral images from the Landsat-8 satellite show that the proposed method
can achieve superior visual effects, and excellent quantitative performance in
terms of spectral distortion, correlation coefficient, MSE, NIQE, BRISQUE, and
PIQE.
