稀疏非负矩阵分解下的遥感图像融合

doi:10.3969/j.issn.1001-2400.2016.02.033

Abstract

Abstract:

In order to reduce the spectral and spatial distortions, a novel method based on sparse non-negative matrix factorization (SNMF) is proposed for multispectral and panchromatic images fusion. Firstly, the high spatial resolution and low spatial resolution dictionaries are learned from panchromatic. Then we construct a sparse non-negative matrix factorization model of the multispectral image. Thus, the coefficients matrix with spectral information can be obtained. The high spatial resolution multispectral image is produced by the multiplication high spatial resolution dictionary and the coefficients matrix. By introducing the sparse regularization, the instability of the standard non-negative matrix factorization is conquered and the fused image can preserve the high spectral and spatial information. Some experiments are made on QuickBird and Geoeye satellite datasets, and experimental results show that our proposed method can reduce distortions in both the spectral and spatial domains, and outperform some related pan-sharpening approaches in visual results and numerical guidelines.

Key words: remote sensing image fusion, non-negative matrix factorization, sparse regularization

LI Hong;LIU Fang;ZHANG Kai. Remote sensing image fusion based on sparse non-negative matrix factorization[J].Journal of Xidian University, 2016, 43(2): 193-198.

References

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Remote sensing image fusion based on sparse non-negative matrix factorization

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