基于最大整体包容度约束非负矩阵分解的高光谱遥感图像混合像元分析算法

Mixed Data Analysis Algorithm Based on Maximum Overall Coverage Constraint Nonnegative Matrix Factorization for Hyperspectral Image

针对高光谱遥感图像中存在高度混合无纯像元的现象,提出了端元整体包容度约束,并将其加入非负矩阵分解的目标函数.在满足端元非负性与和为一约束的同时,利用数据在特征空间的几何特性,要求端元构成的单形体所容纳的像元尽可能多.该算法不需对原始数据降维,不损害数据的物理意义,在迭代过程中使用乘性规则,避免了传统梯度优化过程中常见的整体步长难以控制现象.对模拟图像和真实图像进行实验评测并比较了提取端元精准度、鲁棒性以及执行效率,结果表明,本文算法可有效分析高光谱遥感图像混合像元.

In order to analyze hyperspectral images consisted of highly mixed pixels,a new endmembers overall coverage constraint was proposed and introduced in objective function of nonnegative matrix factorization,which forcely maximizes the number of pixels contained in the simplex constructed by endmembers using data geometrical properties in the feature space while satisfies data nonnegative and abundance sum-to-one constraint simultaneously.In the maximum overall coverage constraint nonnegative matrix factorization algorithm,the dimensionality reduction process is prevented to preserve the physical meaning of the source image and multiplicative update rules are applied to avoid stepsize selection problem occurred in traditional gradient-based optimization algorithm frequently.To evaluate the accuracy of endmembers extraction,the performance and robustness,experiments are designed on synthetic and real images.The results demonstrate that the proposed algorithm is an effective method to analyze mixed data in hyperspectral image.