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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Applications of Independent Component Analysis (ICA) in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery
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Applications of Independent Component Analysis (ICA) in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery

机译：独立分量分析（ICA）在高光谱图像端成员提取和丰度定量中的应用

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Independent component analysis (ICA) has shown success in many applications. This paper investigates a new application of the ICA in endmember extraction and abundance quantification for hyperspectral imagery. An endmember is generally referred to as an idealized pure signature for a class whose presence is considered to be rare. When it occurs, it may not appear in large population. In this case, the commonly used principal components analysis (PCA) may not be effective since endmembers usually contribute very little in statistics to data variance. In order to substantiate our findings, an ICA-based approach, called ICA-based abundance quantification algorithm (ICA-AQA) is developed. Three novelties result from our proposed ICA-AQA. First, unlike the commonly used least squares abundance-constrained linear spectral mixture analysis (ACLSMA) which is a 2nd order statistics-based method, the ICA-AQA is a high order statistics-based technique. Second, due to the use of statistical independence it is generally thought that the ICA cannot be implemented as a constrained method. The ICA-AQA shows otherwise. Third, in order for the ACLSMA to perform abundance quantification, it requires an algorithm to find image endmembers first then followed by an abundance-constrained algorithm for quantification. As opposed to such a two-stage process, the ICA-AQA can accomplish endmember extraction and abundance quantification simultaneously in one-shot operation. Experimental results demonstrate that the ICA-AQA performs at least comparably to abundance-constrained methods.
机译：独立组件分析（ICA）在许多应用程序中均显示出成功。本文研究了ICA在高光谱图像的端成员提取和丰度定量中的新应用。端成员通常被认为是类的理想纯签名，该类的出现被认为很少。发生时，它可能不会出现在大量人群中。在这种情况下，常用的主成分分析（PCA）可能无效，因为终端成员通常在统计上对数据差异的贡献很小。为了证实我们的发现，开发了一种基于ICA的方法，称为基于ICA的丰度量化算法（ICA-AQA）。我们提出的ICA-AQA带来了三个新颖性。首先，与通常使用的最小二乘丰度约束线性光谱混合分析（ACLSMA）是基于二阶统计量的方法不同，ICA-AQA是基于高阶统计量的技术。其次，由于使用统计独立性，通常认为ICA不能作为约束方法来实现。 ICA-AQA显示其他情况。第三，为了使ACLSMA执行丰度量化，需要先找到图像末端成员的算法，然后再寻找丰度受限的算法进行量化。与这样的两阶段过程相反，ICA-AQA可以一次完成操作，同时完成端成员提取和丰度定量。实验结果表明，ICA-AQA的性能至少与丰度受限的方法相当。

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