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

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.