LOW-COMPLEXITY PRINCIPAL COMPONENT ANALYSIS FOR HYPERSPECTRAL IMAGE COMPRESSION

Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based 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 sub-sampling 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.