Low-Complexity Principal Component Analysis for Hyperspectral Image Compression

Abstract

Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCAbased spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral image compression. However, the computational cost of determining the data-dependent PCA transform is high because of its traditional eigendecomposition implementation which requires calculation of a covariance matrix across the data. Several strategies for reducing the computation burden of PCA are explored, including both spatial and spectral subsampling in the covariance calculation as well as an iterative algorithm that circumvents determination of the covariance matrix entirely. Experimental results investigate the impacts of such low-complexity PCA on JPEG2000 compression of hyperspectral images, focusing on rate-distortion performance as well as data-analysis performance at an anomaly-detection task.