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

Abstract

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 preprocessing 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 indicators in terms of spectral distortion, correlation coefficient, MSE, NIQE, BRISQUE, and PIQE.

A Visible and Synthetic Aperture Radar Image Fusion Algorithm Based on a Transformer and a Convolutional Neural Network

“…Currently, mainstream visible and SAR image fusion methods can be broadly categorized into two types, including traditional image fusion methods and deep learningbased image fusion methods. Traditional image fusion algorithms mainly include Laplace Pyramid (LP) [1][2][3], Shear Wave (SW) [4][5][6], Discrete Wavelet Transform (DWT) [7][8][9], Non-Subsampled Shearlet Transform (NSST) [10][11][12], Sparse Representation (SR) [13][14][15][16], and other methods. However, traditional methods use complex transformations and manual rules, thus limiting the real-time performance of the algorithms and the integration of semantic information, which restricts their application in advanced visual tasks.…”