基于约束非负矩阵分解改进的高光谱解混方法

An improved hyperspectral image unmixing method based on constrained non-negative matrix factorization

摘要:

为了进一步缩小非负矩阵分解(non-negative matrix factorization,NMF)的解空间,提出了一种基于保持高光谱图像的内在结构不变性约束和端元光谱分段平滑约束的 NMF的高光谱解混算法。首先,采用一个投影方程来描述高光谱图像的内在结构。然后引入图正则化,建立高光谱图像与丰度矩阵之间的联系,以保持高光谱图像内在结构的不变性。其次,采用马尔科夫随机场模型中的自适应势函数作为促进端元平滑的平滑函数。最后,采用L1/2稀疏约束促进丰度矩阵的 稀疏性。为了验证所提算法的性能,在两个真实数据集上进行了实验分析,证明了该方法的优越性。

Abstract:

In order to further reduce the solution space of non-negative matrix factorization (NMF),a NMF method based on preserving the intrinsic structure of invariant constraint and the piecewise smoothness constraint of the endmember spectrum of the hyperspectral image for hyperspectral unmixing is proposed.Firstly,a projection equation is used to describe the intrinsic structure of the hyperspectral image.Then the graph regularization is introduced to establish the relationship between hyperspectral image and abundance matrix,thereby preserving the intrinsic structure invariant of hyperspectral image.Secondly,the adaptive potential function in Markov random field model is used as a smoothing function to improve the smoothness of the endmembers spectrum.Finally,L1/2 sparse constraint is used to promote the sparsity of the abundance matrix.To validate the performance of the proposed method,an experimental analysis is conducted on two real data sets.The results confirm the superiority of the method.