山东农业大学学报（自然科学版）2024，Vol.55Issue(3)：422-426,5.DOI:10.3969/j.issn.1000-2324.2024.03.014
两阶段非负矩阵分解算法及其在光谱解混中的应用
Two-stage Nonnegative Matrix Factorization Algorithm and Its Application in Hyperspectral Unmixing
摘要
Abstract
The non-negative matrix factorization(NMF)model has been effectively applied in the hyperspectral unmixing.It is known that the objective function's nonconvexity results in the solution's instability.To address this issue,two main streams of approaches have been proposed.Starting NMF with a reasonable initialization or adding regularized terms to NMF.We propose a two-stages NMF method which combines the initialization method and definition of regularized NMF model.In the first stage,k-means is employed to find the initial end-member matrix.In the second stage,a regularized NMF problem is defined,which makes full use of the initial end-member matrix obtained in the first stage.In our algorithm,the initialization matrix of the end-member matrix in the first stage of our algorithm also contributes to the regularized term.The project gradient method is employed to solve the non-negative least square problems alternatively.Numerical results indicate that the new algorithm works well.关键词
非负矩阵分解/正则项/投影梯度法/光谱解混Key words
Nonnegative matrix factorization/regularized term/project gradient method/hyperspectral unmixing分类
信息技术与安全科学引用本文复制引用
杨颂,张新元,刘晓,孙莉..两阶段非负矩阵分解算法及其在光谱解混中的应用[J].山东农业大学学报（自然科学版）,2024,55(3):422-426,5.基金项目
国家自然科学基金(11701337,5227526) （11701337,5227526）
山东省自然科学基金(R2022MA009) （R2022MA009）
