Higher-dimensional wavelet transforms for hyperspectral data compression and feature recognition
James F. Scholl, Eustace L. Dereniak · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
The dominant image processing tasks for hyperspectral data are compression and feature recognition. These tasks go hand-in-hand. Hyperspectral data contains a huge amount of information that need to be processed (and often very quickly) depending on the application. The discrete wavelet transform is the ideal tool for this type of data structure. There are applications that require such processing (especially feature recognition or identification) be done extremely fast and efficiently. Furthermore the higher number of dimensions implies a number of different ways to do these transforms. Much of the work in this area to the present time has been focused on JPEG2000 type compression of each component image involving fairly sophisticated coding techniques; relatively little attention has been paid to other configurations of wavelet transforms of such data, as well as rapid feature identification where compression may not be necessary at all. This paper describes other versions of the 3D wavelet transform that allow the resolution in both the spatial domain and spectral domain to be adjusted separately. Other issues associated with low complexity feature recognition with and without compression using versions of the 3D hyperspectral wavelet transforms will be discussed along with some illustrative calculations.