Nonlocal Low-Rank Regularization for Hyperspectral and High-Resolution Remote Sensing Image Fusion

Fusion of high spatial resolution multispectral images (HR-MSI) and low spatial resolution hyperspectral images (LR-HSI) of the same scene can effectively combine spectral and spatial information to obtain high resolution hyperspectral images (HR-HSI), but it can also cause spectral distortion. To address this problem, we propose a new fusion algorithm (NLLR) based on a combination of low-rank prior and observation model in this paper. In the proposed NLLR method, we incorporate nonlocal spatial similarity and low-rank prior into the fusion problem to better simulate the spatial and spectral features of HR-HSI. By extracting tensor blocks from the hyperspectral and multispectral images, performing a chunking clustering operation on the hyperspectral and mul-tispectral data respectively, and constraining the fusion model using low-rank regularization to transform it into solving a convex optimization problem, followed by iterative optimization of the optimization problem using the alternating direction method of multiplier (ADMM), which can achieve an accurate reconstruction. Experimental results show that NLLR can provide better fusion performance compared to state-of-the-art fusion models.