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.
Multiplicative Update Methods for Incremental Quantile Estimation
