The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior

In this paper, we propose a tensor-based non-convex sparse modeling approach for the fusion of panchromatic and multispectral remote sensing images, and this kind of fusion is generally called pansharpening. We first upsample the low spatial-resolution multispectral image by a classical interpolation method to get an initial upsampled multispectral image. Based on the hyper-Laplacian distribution of errors between the upsampled multispectral image and the ground-truth high resolution multispectral image on gradient domain, we formulate a ℓ_p(0 < p < 1)-norm term to more reasonably describe the relation of these two datasets. In addition, we also model a tensor-based weighted fidelity term for the panchromatic and low resolution multispectral images, aiming to recover more spatial details. Moreover, total variation regularization is also employed to depict the sparsity of the latent high resolution multispectral image on the gradient domain. For the model solving, we design an alternating direction method of multipliers based algorithm to efficiently solve the proposed model. Furthermore, the involved non-convex ℓ_p subproblem is handled by an efficient generalized shrinkage/thresholding algorithm. Finally, extensive experiments on many datasets collected by different sensors demonstrate the effectiveness of our method when compared with several state-of-the-art image fusion approaches.