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Multimedia Tools and Applications

, Volume 78, Issue 6, pp 7563–7578 | Cite as

Pansharpening with support vector transform and semi-nonnegative matrix factorization

  • Hong LiEmail author
  • Weibin Li
  • Shuying Liu
Article
  • 69 Downloads

Abstract

This paper attempts to reduce the spectral distortion and enhance the spatial information of fused images. For this purpose, the author presented a novel pansharpening method based on support vector transform (SVT) and semi-nonnegative matrix factorization (semi-NMF). The proposed method involves three steps. In step one, SVT was performed on panchromatic and multispectral images. In step two, the low-frequency components were processed by semi-NMF-based fusion rule, while the high-frequency components were treated by the regional energy-weighted fusion rule. In step three, the fused images were reconstructed by the fused high-frequency and low-frequency components. After that, the proposed method was compared with other related methods through experiments on several datasets collected from QuickBird and GeoEye-1. The comparison shows that the proposed method outperforms the compared approaches. The research findings shed new light on the preservation of spatial and spectral information in image fusion.

Keywords

Pansharpening Panchromatic and multispectral images Nonnegative matrix factorization (NMF) Support vector transform (SVT) 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for suggesting various changes. And this work was supported by the National Natural Science Foundation of China (No. 81473559), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2017JM6086), the Science Basic Research Program in Shaanxi Province of China (No. 16JK1823), the Innovation Program in Shaanxi Province of China (No. 2018KRM145), the Science Basic Research Program in Xianyang Normal University of China (No. XSYK18012).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceXianYang Normal UniversityXianYangChina

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