Spatial–Spectral-Graph-Regularized Low-Rank Tensor Decomposition for Multispectral and Hyperspectral Image Fusion

Abstract

Hyperspectral (HS) and multispectral (MS) image fusion aims at producing high-resolution HS (HRHS) images. However, the existing methods could not simultaneously consider the structures in both the spatial and spectral domains of the HS cube. In order to effectively preserve spatial–spectral structures in HRHS images, we propose a new low-resolution HS (LRHS) and high-resolution MS (HRMS) image fusion method based on spatial–spectral-graph-regularized low-rank tensor decomposition (SSGLRTD) in this paper. First, we reformulate the image fusion problem as a low-rank tensor decomposition model to utilize the low-rank property in the HS image. Then, two graphs are constructed in spatial and spectral domains, respectively. One of them is derived from the HRMS image for the spatial consistency, and the other is inferred from the LRHS image for spectral smoothness. Finally, the SSGLRTD fusion framework is established by combining all regularizers. With these two graphs, the spatial correlation and the spectral structure in the fused HRHS images are efficiently preserved. The experimental results on different datasets reveal that the proposed fusion method outperforms several existing fusion methods in terms of visual analysis and numerical comparison.