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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Exploration of Virtual Dimensionality in Hyperspectral Image Analysis
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Exploration of Virtual Dimensionality in Hyperspectral Image Analysis

机译：高光谱图像分析中虚拟维数的探索

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摘要

Virtual dimensionality (VD) is a new concept which was developed to estimate the number of spectrally distinct signatures present in hyperspectral image data. Unlike intrinsic dimensionality which is mainly of theoretical interest, the VD is a very useful and practical notion. It is derived from the Neyman-Pearson detection theory. Unfortunately, its utility in hyperspectral data exploitation has yet to be explored. This paper presents several applications to which the VD is applied successfully. Since the VD is derived from a binary hypothesis testing problem for each spectral band, it can be used for band selection. When the test fails for a band, it indicates that there is a signal source in that particular band which must be selected. By the same token it can be further used for dimensionality reduction. For principal components analysis (PCA) or independent component analysis (ICA), the VD helps to determine the number of principal components or independent components are required for exploitation such as detection, classification, compression, etc. For unsupervised target detection and classification, the VD can be used to determine how many unwanted signal sources present in the image data so that they can be eliminated prior to detection and classification. For endmember extraction, the VD provides a good estimate of the number of endmembers needed to be extracted. All these applications are justified by experiments.
机译：虚拟维数（VD）是一个新概念，用于估计高光谱图像数据中存在的光谱不同特征的数量。与主要具有理论意义的固有维数不同，VD是一个非常有用且实用的概念。它源自Neyman-Pearson检测理论。不幸的是，它在高光谱数据开发中的效用尚待探索。本文介绍了已成功将VD应用到的几个应用程序。由于VD是从每个频谱带的二进制假设测试问题得出的，因此可以将其用于频带选择。如果某个频段的测试失败，则表明该特定频段中有一个信号源必须选择。同样，它可以进一步用于降维。对于主成分分析（PCA）或独立成分分析（ICA），VD可帮助确定开发所需的主成分或独立成分的数量，例如检测，分类，压缩等。对于无监督的目标检测和分类， VD可用于确定图像数据中存在多少个不需要的信号源，以便可以在检测和分类之前将它们消除。对于最终成员提取，VD可以很好地估计需要提取的最终成员数量。所有这些应用都通过实验证明是合理的。

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