Segmented Principal Component Analysis for Parallel Compression of Hyperspectral Imagery

Principal component analysis (PCA) is widely used for spectral decorrelation in the JPEG2000 compression of hyperspectral imagery. However, due to the data-dependent nature of principal components, the principal component transform matrix is stored in the JPEG2000 bitstream, constituting an overhead that is often negligible if the spatial size of the image is large. However, in parallel compression in which the data set is partitioned to multiple independent processing nodes, the overhead may no longer remain negligible. It is shown that a segmented approach to PCA can greatly mitigate the detrimental effects of transform-matrix overhead and can outperform wavelet-based decorrelation which entails no such overhead.