Joint Sparse Representations and Coupled Dictionary Learning in Multisource Heterogeneous Image Pseudo-Color Fusion

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. First, the traditional Brovey transform is employed as a preprocessing method on the paired SAR and multispectral images. Then, CDL is used to capture the correlation between the preprocessed 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 indicators in terms of spectral distortion, correlation coefficient, mean square error (mse), natural image quality evaluator (NIQE), Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE), and perception-based image quality evaluator (PIQE).