IEEE Trans Image Process. 2015 Dec;24(12):4810-9. doi: 10.1109/TIP.2015.2468177. Epub 2015 Aug 13.
We introduce a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. The new model extends the commonly used linear mixing model by introducing an additional term accounting for possible nonlinear effects, that are treated as sparsely distributed additive outliers. With the standard nonnegativity and sum-to-one constraints inherent to spectral unmixing, our model leads to a new form of robust nonnegative matrix factorization with a group-sparse outlier term. The factorization is posed as an optimization problem, which is addressed with a block-coordinate descent algorithm involving majorization-minimization updates. Simulation results obtained on synthetic and real data show that the proposed strategy competes with the state-of-the-art linear and nonlinear unmixing methods.
我们引入了一个强大的混合模型来描述由几种纯光谱特征混合而成的高光谱数据。新模型通过引入一个额外的项来扩展常用的线性混合模型，该项用于表示可能的非线性效应，这些效应被视为稀疏分布的附加异常值。利用光谱解混中固有的标准非负性和总和为一的约束，我们的模型导致了一种新的鲁棒非负矩阵分解形式，其中包含具有群组稀疏异常值项。该分解被表示为一个优化问题，通过涉及最大化-最小化更新的分块坐标下降算法来解决。在合成和真实数据上获得的模拟结果表明，所提出的策略与最先进的线性和非线性解混方法相竞争。
