Parallel Data Compression for Hyperspectral Imagery

The high dimensionality of hyperspectral imagery challenges image processing and analysis. It has been shown that hyperspectral compression can be achieved by principal component analysis (PCA) for spectral decorrelation followed by the JPEG2000-based coding. This approach, referred to as PCA+JPEG2000, provides superior rate-distortion performance and can preserve useful data information. However, its main disadvantage is high computational complexity in the PCA process which entails the calculation of the data covariance matrix and its eigenvectors. Parallel processing is an appropriate approach to relieve the computation burden of such a PCA-based compression. In this paper, several parallel PCA implementations are proposed and their processing speed and resulting compression performance are investigated.