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

Liang Jian Deng*, Minyu Feng, Xue-Cheng TAI

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

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 ℓp(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 ℓp 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.

Original languageEnglish
Pages (from-to)76-89
Number of pages14
JournalInformation Fusion
Volume52
DOIs
Publication statusPublished - Dec 2019

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

User-Defined Keywords

  • Alternating direction method of multipliers
  • hyper-Laplacian
  • Pansharpening
  • Tensor-based sparse modeling

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