基于自适应局部邻域加权约束的非负矩阵分解方法及其在高光谱解混中的应用

非负矩阵分解(Nonnegative Matrix Factorization,NMF)技术已经成为了高光谱解混领域研究的热点.但是如何有效地利用高光谱的空间和光谱信息仍然是一个难点,尤其在确定局部邻域时,往往会遇到结构固定等问题.针对以上问题,提出了一种基于自适应局部邻域加权约束的非负矩阵分解算法.算法根据丰度的数据...

A Nonnegative Matrix Factorization Method Based on Adaptive Local Neighborhood Weighted Constraint and Its Application in Hyperspectral Unmixing

Nonnegative Matrix Factorization (NMF) had become a hot research topic in the field of hyperspectral unmixing. However, it was still difficult to make effective use of hyperspectral space and spectral information, especially when determining local neighborhood, structure fixation was often encountered. To solve the above problems, a non-negative matrix decomposition algorithm based on adaptive local neighborhood weighting constraint was proposed. The local neighborhood of a given pixel could be determined adaptively according to the data characteristics of the abundance. The weight of the algorithm made full use of the spatial and spectral information of the given pixel and the pixel in the neighborhood to improve the performance of hyperspectral unmixing. In this paper, the iterative rule of multiplication was derived by gradient descent method. In order to verify the effectiveness of the proposed algorithm, Japser Ridge data set and Urban data set were used for experiments, and compared with other classical methods, the results showed that this method had better unmixing effect.