Component analysis of spatial and spectral patterns in multispectral images. II. Entropy minimization.
Component analysis of spatial and spectral patterns in multispectral images. II. Entropy minimization.
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多光谱图像中空间和光谱模式的成分分析。
DOI：
10.1364/josaa.6.000073
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发表时间：
1989
期刊：
影响因子：
--
通讯作者：
Shigeo Minami
中科院分区：
文献类型：
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作者：
K. Sasaki;Satoshi Kawata;Shigeo Minami
在第一部分中[J.选择。苏克。是。在本系列的第 4, 2101 (1987)] 中，我们开发了一种用于估计多光谱场景中组件的空间模式和光谱曲线的方法。该方法不需要有关成分的空间和光谱信息，而只需要多重扩展图像数据。该估计被给出为满足所有像素处所有分量的密度和光谱响应的非负性约束的可行解集。在本文中，我们从可行解集中估计了分量模式和光谱的唯一解。通过优化熵最小化准则给出解决方案。该标准增强了各个成分的光谱或空间特征。两个实验结果证明了该方法对生物和细胞化学样本的有效性。还讨论了这种独特模式估计方法的局限性。
In Part I [J. Opt. Soc. Am. A 4, 2101 (1987)] of this series, we developed a method for estimating both spatial patterns and spectral curves of components in a multispectral scene. This method does not need spatial and spectral information about the components but only multispread imagery data. The estimation is given as a feasible solution set satisfying the nonnegativity constraint for density and spectral response for all components at all pixels. In this paper, we estimate unique solutions for both the component patterns and the spectra from the feasible solution set. The solution is given by optimizing an entropy minimization criterion. This criterion enhances the spectral or spatial features of individual components. Two experimental results are shown to demonstrate the effectiveness of this method with biological and cytochemical specimens. The limitations of this method for unique pattern estimation are also discussed.