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首页> 外文期刊>Journal of Chemometrics >Blind decomposition of low-dimensional multi-spectral image by sparse component analysis
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Blind decomposition of low-dimensional multi-spectral image by sparse component analysis

机译：稀疏成分分析法对低维多光谱图像进行盲分解

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摘要

A multilayer hierarchical alternating least square nonnegative matrix factorization approach has been applied to blind decomposition of low-dimensional multi-spectral image. The method performs blind decomposition exploiting spectral diversity and spatial sparsity between materials present in the image and, unlike many blind source separation methods, is invariant with respect to statistical (in)dependence among spatial distributions of the materials. As opposed to many existing blind source separation algorithms, the method is capable of estimating the unknown number of materials present in the image. This number can be less than, equal to, or greater than the number of spectral bands. The method is validated on underdetermined blind source separation problems associated with blind decomposition of experimental red-green-blue images composed of four materials. Achieved performance has been superior when compared against methods based on minimization of the l1-norm: linear programming and interior-point methods. In addition to tumor demarcation, as demonstrated in the paper, other areas that can also benefit from the proposed method include cell, chemical, and tissue imaging.
机译：多层层次交替最小二乘非负矩阵分解方法已应用于低维多光谱图像的盲分解。该方法利用图像中存在的材料之间的光谱多样性和空间稀疏性执行盲分解，并且与许多盲源分离方法不同，该方法相对于材料空间分布之间的统计（不相关性）是不变的。与许多现有的盲源分离算法相反，该方法能够估计图像中存在的未知数量的材料。该数目可以小于，等于或大于光谱带的数目。该方法在与由四种材料组成的实验性红绿蓝图像的盲分解相关的不确定盲源分离问题上得到了验证。与基于l1范数最小化的方法（线性编程和内点方法）相比，所获得的性能要优越。如本文所述，除了肿瘤分界外，还可从提议的方法中受益的其他领域包括细胞，化学和组织成像。

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