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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Sample Spectral Correlation-Based Measures for Subpixels and Mixed Pixels in Real Hyperspectral Imagery
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Sample Spectral Correlation-Based Measures for Subpixels and Mixed Pixels in Real Hyperspectral Imagery

机译：基于样本光谱相关性的真实高光谱图像中子像素和混合像素的度量

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摘要

A hyperspectral imaging sensor images a scene using hundreds of contiguous spectral channels to uncover many substances that cannot be resolved by multspectral sensors with tens of discrete spectral channels. Many spectral measures used for target discrimination and identification in hyperspectral imagery have been derived directly from multsispectral imagery rather than from a hyperspectral imagery viewpoint. This paper demonstrates that on many occasions such spectral measures are generally not effective when it is applied to real hyperspectral data for discrimination and identification due to the fact that they do not take into account the very high sample spectral correlation (SSC) provided by hyperspectral sensors. In order to address this issue, two approaches, referred to as a priori sample spectral correlation (PR-SSC) and a posteriori SSC (PS-SSC) are developed to account for spectral variability within real data to achieve better target discrimination and identification. While the former can be used to derive a family of a priori hyperspectral measures via orthogonal subspace projection (OSP) to eliminate interfering effects caused by undesired signatures, the latter results in a family of a posteriori hyperspectral measures that include sample covariance/correlation matrix as a posteriori information to increase ability in discrimination and identification. Interestingly, some well-known measures such as Euclidean distance (ED) and spectral angle mapper (SAM) can be shown to be special cases of the proposed PR-SSC and PS-SSC hyperspectral measures.
机译：高光谱成像传感器使用数百个连续光谱通道对场景进行成像，以发现许多具有数十个离散光谱通道的多光谱传感器无法解析的物质。高光谱成像中用于目标识别和识别的许多光谱测量都是直接从多光谱成像而不是从高光谱成像的角度得出的。本文表明，在许多情况下，将此类光谱测量应用于真实的高光谱数据进行判别和识别时通常是无效的，因为它们没有考虑到高光谱传感器提供的非常高的样品光谱相关性（SSC） 。为了解决这个问题，开发了两种方法，称为先验样本光谱相关（PR-SSC）和后验SSC（PS-SSC），以解决真实数据中的光谱变异性，以实现更好的目标识别和识别。前者可用于通过正交子空间投影（OSP）来导出先验高光谱测度的族，以消除由不想要的特征引起的干扰效应，而后者可导致后验高光谱测度的族，其中包括样本协方差/相关矩阵，如后验信息，以提高辨别和识别的能力。有趣的是，一些众所周知的量度，例如欧几里德距离（ED）和光谱角映射器（SAM），可以证明是拟议PR-SSC和PS-SSC高光谱量度的特例。

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