Generalized bilinear model based nonlinear unmixing using semi-nonnegative matrix factorization
Generalized bilinear model based nonlinear unmixing using semi-nonnegative matrix factorization
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DOI：
10.1109/igarss.2012.6351282
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发表时间：
2012-07
期刊：
影响因子：
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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最近在高光谱图像处理中受到关注。这项工作提出了一种基于广义双线性模型（GBM）的非线性解混合的新颖优化方法，该方法考虑了二阶散射效应。半非负矩阵分解用于优化以矩阵形式处理整个图像。所提出的方法应用于具有许多端元的机载高光谱图像，并且在解混合质量和计算成本方面表现出良好的性能，并且实现简单。研究了端元提取对非线性解混合的影响，并证明了非线性对丰度图的影响。
Nonlinear spectral mixing models have recently been receiving attention in hyperspectral image processing. This work presents a novel optimization method for nonlinear unmixing based on a generalized bilinear model (GBM), which considers second-order scattering effects. Semi-nonnegative matrix factorization is used for optimization to process a whole image in a matrix form. The proposed method is applied to an airborne hyperspectral image with many endmembers and shows good performance both in unmixing quality and computational cost with simple implementation. The effect of endmember extraction on nonlinear unmixing is investigated and the impact of the nonlinearity on abundance maps is demonstrated.