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Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging

机译：结构化协方差主成分分析，用于高光谱成像中的实时现场特征提取和降维

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摘要

Presented in a 3-D structure called hypercube, hyperspectral imaging (HSI) suffers from large volume of data and high computational cost for data analysis. To overcome such drawbacks, principal component analysis (PCA) has been widely applied for feature extraction and dimensionality reduction. However, a severe bottleneck is how to compute the PCA covariance matrix efficiently and avoid computational difficulties, especially when the spatial dimension of the hypercube is large. In this paper, structured covariance PCA (SC-PCA) is proposed for fast computation of the covariance matrix. In line with how spectral data is acquired in either the push-broom or tunable filter way, different implementation schemes of SC-PCA are presented. As the proposed SC-PCA can determine the covariance matrix from partial covariance matrices in parallel even without deducting the mean vector in prior, it facilitates real-time data analysis whilst the hypercube is acquired. This has significantly reduced the scale of required memory and also allows efficient onsite feature extraction and data reduction to benefit subsequent tasks in coding/compression, transmission, and analytics of hyperspectral data.
机译：高光谱成像（HSI）以称为超立方体的3-D结构呈现，它具有数据量大和数据分析的计算成本高的缺点。为了克服这些缺点，主成分分析（PCA）已广泛用于特征提取和降维。然而，严重的瓶颈是如何有效地计算PCA协方差矩阵并避免计算困难，尤其是在超立方体的空间尺寸较大时。本文提出了结构化协方差PCA（SC-PCA）来快速计算协方差矩阵。根据如何以推扫式或可调谐滤波器方式获取光谱数据，提出了SC-PCA的不同实现方案。由于所提出的SC-PCA甚至可以在不扣除均值向量的情况下从部分协方差矩阵中并行确定协方差矩阵，因此有助于在获取超立方体的同时进行实时数据分析。这显着减少了所需内存的规模，还允许有效的现场特征提取和数据缩减，从而有利于后续的编码/压缩，传输和高光谱数据分析任务。

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