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Frontiers of Earth Science

, Volume 13, Issue 3, pp 656–667 | Cite as

A pan-sharpening method based on the ADMM algorithm

  • Yingxia Chen
  • Tingting Wang
  • Faming Fang
  • Guixu ZhangEmail author
Research Article
  • 21 Downloads

Abstract

Pan-sharpening is a method of integrating low-resolution multispectral images with corresponding high-resolution panchromatic images to obtain multispectral images with high spectral and spatial resolution. A novel variational model for pan-sharpening is proposed in this paper. The model is mainly based on three hypotheses: 1) the pan-sharpened image can be linearly represented by the corresponding panchromatic image; 2) the low-resolution multispectral image is down-sampled from the highresolution multispectral image through the down-sampling operator; and 3) the satellite image has the low-rank property. Three energy components corresponding to these assumptions are integrated into a variational framework to obtain a total energy function. We adopt the alternating direction method of multipliers (ADMM) to optimize the total energy function. The experimental results show that the proposed method performs better than other mainstream methods in spectral and spatial information preserving aspect.

Keywords

pan-sharpening multispectral image panchromatic image variational framework energy function ADMM 

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Notes

Acknowledgements

We sincerely thank the editor and reviewers for their insightful comments and constructive suggestions on the article, and thank Dr. Thomas James Godfrey for helping us to revise the grammar. This work was supported in part by “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (Grant No. 17CG25), in part by the Key Project of the National Natural Science Foundation of China (Grant No. 61731009), and in part by the National Natural Science Foundation of China (Grant No. 61871185).

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yingxia Chen
    • 1
    • 2
  • Tingting Wang
    • 1
  • Faming Fang
    • 1
  • Guixu Zhang
    • 1
    Email author
  1. 1.Department of Computer ScienceEast China Normal UniversityShanghaiChina
  2. 2.School of Computer ScienceYangtze UniversityJingzhouChina

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