引用本文: 王楠, 张良培, 杜博. 最小光谱相关约束NMF的高光谱遥感图像混合像元分解[J]. 武汉大学学报 ( 信息科学版), 2014, 39(1): 22-26.
WANG Nan, ZHANG Liangpei, DU Bo. Minimum Spectral Correlation Constraint Algorithm Based on Non-negativeMatrix Factorization for Hyperspectral Unmixing[J]. Geomatics and Information Science of Wuhan University, 2014, 39(1): 22-26.
 Citation: WANG Nan, ZHANG Liangpei, DU Bo. Minimum Spectral Correlation Constraint Algorithm Based on Non-negativeMatrix Factorization for Hyperspectral Unmixing[J]. Geomatics and Information Science of Wuhan University, 2014, 39(1): 22-26.

## Minimum Spectral Correlation Constraint Algorithm Based on Non-negativeMatrix Factorization for Hyperspectral Unmixing

• 摘要: 提出了一种最小化光谱相关度约束的非负矩阵分解方法。该方法根据高光谱遥感图像中端元之间具有不相关性这一特点,提出了一种光谱相关度函数。该函数度量光谱之间的相关程度,函数值越小,光谱间的相关度越小。通过联合最小化光谱相关度函数和非负矩阵分解误差函数,使获得的光谱之间具有最小的相关性,从而获得端元光谱以及组分图。模拟实验和真实实验证明了算法的有效性。

Abstract: This paper proposes minimum spectral correlation constraint algorithm based on non-nega-tive matrix factorization(MSCCNMF).According to the properties of non-correlation among the endmembers in a hyperspectral image,a spectral correlation function is proposed to measure the degree ofcorrelation between spectral signatures.The smaller the function value,the smaller the correlationbetween the spectra.By minimizing the spectral correlation function with the error function of non-negative matrix factorization,a set of spectra with smallest correlation can be obtained,considered asendmembers,and the corresponding abundances can be obtained simultaneously.Synthetic and realexperiments show the effectiveness of the proposed algorithm.

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