Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization
Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization
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DOI：
10.1109/tgrs.2013.2251349
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发表时间：
2014-02
影响因子：
8.2
通讯作者：
N. Yokoya;J. Chanussot;A. Iwasaki
中科院分区：
文献类型：
--
作者：
N. Yokoya;J. Chanussot;A. Iwasaki
非线性光谱混合模型在高光谱图像处理中受到了广泛的关注。在本文中，我们提出了一种新的优化方法的非线性解混合的基础上的广义双线性模型（GBM），它考虑了二阶散射的光子的光谱混合模型。半非负矩阵分解（semi-NMF）用于优化以矩阵形式处理整个图像。当端元谱给定时，通过交替更新规则，优化丰度和相互作用丰度分数收敛到局部最优值，实现简单。所提出的方法进行评估，使用合成数据集考虑其鲁棒性的端元提取和光谱复杂性的准确性，并显示丰度分数，而不是传统的方法更小的误差。基于GBM的解混使用半NMF的机载高光谱图像的分析，采取了农业领域与许多端元，它可视化的影响，在合理的计算成本的非线性相互作用的丰度图。
Nonlinear spectral mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel optimization method of nonlinear unmixing based on a generalized bilinear model (GBM), which considers the second-order scattering of photons in a spectral mixture model. Semi-nonnegative matrix factorization (semi-NMF) is used for the optimization to process a whole image in matrix form. When endmember spectra are given, the optimization of abundance and interaction abundance fractions converge to a local optimum by alternating update rules with simple implementation. The proposed method is evaluated using synthetic datasets considering its robustness for the accuracy of endmember extraction and spectral complexity, and shows smaller errors in abundance fractions rather than conventional methods. GBM-based unmixing using semi-NMF is applied to the analysis of an airborne hyperspectral image taken over an agricultural field with many endmembers, and it visualizes the impact of a nonlinear interaction on abundance maps at reasonable computational cost.