基于非负矩阵分解的高光谱遥感图像混合像元分解

传统非负矩阵分解算法的目标函数具有大量的局部极小,在进行高光谱图像的光谱解混时,受初始值的影响很大.为解决该问题,作者通过在目标函数中引入丰度分离性和平滑性的约束条件,提出一种基于有约束非负矩阵分解的混合像元分解方法.同时该算法能够满足混合像元分解问题所要求的丰度值非负以及和为一的约束.模拟和实际数据实验结果表明,所提...

Hyperspectral unmixing based on nonnegative matrix factorization

Because of the local minima in the objective function, the traditional Nonnegative Matrix Factorization (NMF) algorithm is sensitive to the initial value when being applied to hyperspectral unmixing. In order to solve the problem, this paper propose a new approach based on constrained NMF for decomposition of mixed pixels by introducing constraints of abundance separation and smoothness into the objective function of NMF. The algorithm can also satisfy the abundance nonnegative and sum-to-one constraints, which are necessary for hyperspectral unmixing. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach can overcome the shortcoming of local minima, and obtain better results. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used for hyperspectral data in which pure pixels do not exist.