Abstract
Hyperspectral imaging sensors are continuously enhanced by increasing spatial and spectral resolution. However, the ‘curse of dimensionality’ comes into effect, as the amount of acquired data increases in third order (two spatial, one spectral dimension). On top of that, the computational expense of many chemometric data evaluation techniques such as principal component analysis (PCA) rises also in third order with the data amount. Thus the need for computer memory is increased in third and the demand for computational power up to ninth order. One immediate consequence of massively increased computation times is a decreased time resolution of hyperspectral imagers, which is especially detrimental in online applications. Archiving humongous amounts of data becomes another burden. Thus it is anticipated that increased computation resources alone will not be able to facilitate fast online evaluation of hyperspectral data cubes or even to handle the acquired data. In this study, 3D wavelet transforms are used for compression of hyperspectral data cubes prior to chemometric data evaluation and/or data storage. Five different wavelet types of the Daubechies family are compared with respect to their ability to preserve relevant information in compressed data cubes. Because only relevant information has to be considered, shorter computation time and less storage space are required. By means of experimental hyperspectral data cubes it is demonstrated that additional computation expense for the 3D wavelet transform is largely overcompensated by reduced calculations necessitated by smaller data cubes. In the presented examples, computation time for a PCA could be decreased by one order of magnitude; storage space could be reduced to <1% of the original size. It was found that the wavelet type has a major influence on the acceleration factors. Copyright © 2005 John Wiley & Sons, Ltd.