光谱重建约束非负矩阵分解的高光谱与全色图像融合

doi:10.11896/jsjkx.200900054

Abstract

Abstract: An effective algorithm for unmixing hyperspectral and panchromatic images of non-negative matrix factorization based on spectral reconstruction constraint is proposed.Firstly,this algorithm employs the regularization with minimum spectral reconstruction error in the process of non-negative matrix factorization for the hyperspectral image,and searches for the optimal regularization parameter through multi-objective optimization to inspire the spectral signature matrix to contain more real spectral features.Then,the panchromatic image is factorized by non-negative matrix to obtain the abundance matrix with the details of the image.Finally,the fusion result is reconstructed by using the spectral signature matrix and the abundance matrix.The experimental results show that the fusion result of the proposed algorithm maintains more details of panchromatic images and effectively decreases spectral distortion simultaneously.It has better performance in both visual effects and objective evaluation than traditional algorithms.

Key words: Hyperspectral and panchromatic image, Image fusion, Multi-objective optimization, Non-negative factorization, Spectral reconstruction constraint

CLC Number:

TP391

GUAN Zheng, DENG Yang-lin, NIE Ren-can. Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion[J].Computer Science, 2021, 48(9): 153-159.

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Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion

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