基于丰度约束核非负矩阵分解的高光谱图像非线性解混

Nonlinear Unmixing for Hyperspectral Imagery Based on Kernel Nonnegative Matrix Factorization with Constraints on Abundances

本文提出一种基于丰度约束核非负矩阵分解的高光谱图像非线性解混方法.通过该方法将原始高光谱图像数据映射到高维特征空间中,使非线性数据在高维空间中变得线性可分.然后,在高维特征空间中,通过线性的非负矩阵分解进行无监督的高光谱解混.同时依据地物分布的空间特性,在丰度上添加稀疏和平滑约束.模拟和真实高光谱图像数据的实验结果表明,与其他解混方法相比,该方法考虑了地物的空间分布特性,提高了在不同的非线性混合场景下的高光谱解混精度.

A nonlinear unmixing algorithm for hyperspectral images based on kernel nonnegative matrix factorization with constraints on abundances is proposed in this paper.The original hyperspectral image data is mapped into a high-dimensional feature space through a kernel function,enabling the nonlinear data become linearly separable in high-dimensional feature space.Then,linear nonnegative matrix factorization is applied for unsupervised hyperspectral unmixing in the high-dimensional feature space.Furthermore,sparseness and smoothness constraints are added on abundances according to the spatial characteristics of the distribution of ground objects.Experimental results on simulated and real hyperspectral data indicate that,compared to other unmixing methods,the proposed algorithm has considered the distribution characteristics of ground objects and can improve the unmixing accuracy in different nonlinear mixing scenarios.