Wavelet lossless compression of ultraspectral sounder data

This paper provides a study concerning the suitability of well-known image coding techniques originally devised for lossy compression of still natural images when applied to lossless compression of ultraspectral sounder data. An ultraspectral sounder generates an unprecedented amount of 3D data, consisting of two spatial and one spectral dimensions; with ultraspectral sounder data, better inference of atmospheric, cloud and surface parameters is feasible. Here we present the experimental results of five widespread wavelet-based coding techniques, namely EZW, IC, SPIHT, JPEG2000 and CCSDS-IDC. Since the considered still image coding techniques are 2D in nature, but the ultraspectral data is 3D, a preprocessing step is applied to convert the two spatial dimensions into a single one. We are also interested in analyzing the benefits of applying some pre-processing step (e.g., linear prediction or bias adjusted reordering) prior to the coding process in order to further exploit the spectral correlation, which is much stronger than the spatial correlation.