Sparsity-Constrained NMF Algorithm Based on Evolution Strategy for Hyperspectral Unmixing
As a powerful and explainable blind separation tool,non-negative matrix factorization(NMF)is attracting increasing attention in Hyperspectral Unmixing(HU).By effectively utilizing the sparsity priori of data,sparsity-constrained NMF has become a representative method to improve the precision of unmixing.However,the optimization technique based on simple multiplicative update rules makes its unmixing results easy to fall into local minimum and lack of robustness.To solve these problems,this paper proposes a new hybrid algorithm for sparsity constrained NMF by intergrating evolutionary computing and multiplicative update rules(MURs).To find the superior solution in each iteration,the proposed algorithm effectively combines the MURs based on alternate optimization technique,the coefficient matrix selection strategy with sparsity measure,as well as the global optimization technique for basis matrix via the differential evolution algorithm.The effectiveness of the proposed method is demonstrated via the experimental results on real data and comparison with representative algorithms.
Ning ShangBin Zuo FengChao
The School of Computer Science,Liaocheng University,252000 LiaoCheng,Shan Dong,China
国际会议
上海
英文
1-4
2018-10-12（万方平台首次上网日期，不代表论文的发表时间）