Hyperspectral sounder data is a particular class of data that requires high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characterization, cloud property and trace gas information. Therefore compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral data that will be generated by the hyperspectral sounders such as AIRS, CrIS, IASI, GIFTS and HES instruments, the use of robust data compression techniques will be beneficial to data transfer and archive.

This paper reviews wavelet-based lossless data compression schemes for the 3D GIFTS and HES data using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are then processed with various 3D zerotree coding schemes such as EZW and SPIHT, followed by various entropy coding. We extend the 3D zerotree coding schemes to take on any size of satellite data, each of whose dimensions need not be divisible by 2^N, where N is the layers of the wavelet decomposition being performed. The 2D wavelet-based JPEG-2000 compression scheme and some other prediction-based 2D lossless compression schemes such as CALIC, JPEG-LS and FELICS are also investigated. Their compression ratios are presented.