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Pansharpening with support vector transform and semi-nonnegative matrix factorization

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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.

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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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  1. School of Computer Science, XianYang Normal University, XianYang, Shaanxi, China

    Hong Li, Weibin Li & Shuying Liu

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  1. Hong Li
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Correspondence to Hong Li.

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Li, H., Li, W. & Liu, S. Pansharpening with support vector transform and semi-nonnegative matrix factorization. Multimed Tools Appl 78, 7563–7578 (2019). https://doi.org/10.1007/s11042-018-6499-y

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