自动化学报2017，Vol.43Issue(12)：2141-2159,19.DOI:10.16383/j.aas.2017.c160653
一种自适应鲁棒最小体积高光谱解混算法
A Robust Minimum Volume Based Algorithm with Automatically Estimating Regularization Parameters for Hyperspectral Unmixing
摘要
Abstract
Hyperspectral unmixing aims at finding hidden endmembers and their corresponding abundances from hy-perspectral images with low spatial resolution. Based on the well-known minimum volume (MV) rule in geometrical based approaches,a robust minimum volume based algorithm with automatically estimating regularization parameters for hyperspectral unmixing(RMVHU)is proposed. In this algorithm,the ANC constraint is replaced with a negative number punished regularizer which may lead to a more robust result to outliers and noise. A cyclic minimization algorithm is used to split the nonconvex RMVHU problem into convex subproblems, and ADMM is referred to sovle the large scale optimization problem with the increasing number of pixels in the image. To improve the convergence of the algorithm, a strategy to estimate the regularization parameters of the regularizer automatically is proposed. Compared with some existing geometrical based methods,experimental results show the superiority of the RMVHU algorithm on both synthetic datasets and real datasets.关键词
高光谱解混/交替方向乘子法/凸优化/最小体积/自适应估参Key words
Hyperspectral unmixing/alternating direction method of multipliers(ADMM)/convex optimization/mini-mum volume/automatically estimating regularization parameters引用本文复制引用
王天成,刘相振,董泽政,王海波..一种自适应鲁棒最小体积高光谱解混算法[J].自动化学报,2017,43(12):2141-2159,19.
