Learning spatial-spectral dual adaptive graph embedding for multispectral and hyperspectral image fusion

Fusion of high spatial resolution multispectral (HR MS) and low spatial resolution hyperspectral (LR HS) images has become a significant way to produce high spatial resolution hyperspectral (HR HS) images. Though many methods have exploited the spatial nonlocal similarity (SNS) and spectral band correlation (SBC) in the HR HS image, it is difficult to model the priors explicitly because the HR HS image is unavailable in real scenes. As the low-dimensional degradation versions, HR MS and LR HS images inherit the SNS and SBC in the HR HS image, respectively. But these methods seldom consider the inheritance of SNS and SBC between the two source images and the HR HS image. In this paper, we propose a spatial–spectral dual adaptive graph embedding model (SDAGE) to exploit the SNS and SBC in HR MS and LR HS images for the regularization of their fusion. Specifically, spatial and spectral graphs are constructed adaptively to describe the SNS in the HR MS image and the SBC in the LR HS image. Then, the two graphs are embedded into the features for the reconstruction of the HR HS image. In this way, it is explicit to ensure the consistency of SNS and SBC between the source images and the HR HS image. Experiments on three benchmark datasets demonstrate the effectiveness of our SDAGE method. The code can be downloaded from https://github.com/RSMagneto/SDAGE.