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Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

机译:鲁棒非负矩阵分解的非线性高光谱解混

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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.
机译:我们引入了一个鲁棒的混合模型来描述由几个纯光谱特征混合而成的高光谱数据。新模型通过引入考虑可能的非线性效应的附加项扩展了常用的线性混合模型,这些非线性项被视为稀疏分布的加法离群值。借助标准非负性和频谱解混所固有的总和一约束,我们的模型导致了一种新的形式,即具有组稀疏异常项的鲁棒非负矩阵分解。分解是一个优化问题,可以通过涉及主要化-最小化更新的块坐标下降算法来解决。在合成数据和实际数据上获得的仿真结果表明,该策略可与最新的线性和非线性分解方法相媲美。

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