Recent achievements in lossless compression of hyperspectral data

Algorithms for compression of hyperspectral data are commonly evaluated on a readily available collection of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. The calibrated images collected in 1997 show sample value distributions which contain artificial regularities introduced by the conversion of raw data values to radiance units. Being optimal on images having flat histograms, classical DPCM methods do not work in their best conditions. Conversely, the lower lossless bit rates are achieved by algorithms based on lookup-table (LUT) that significantly exploit these artifacts. This singular behavior has not been widely reported and may not be widely recognized. The main consequence is these performances can be misleading if they are extrapolated to images that lack such artifacts. In fact, LUT-based algorithms are not able to achieve the best compression performances on a set of more recent (2006) AVIRIS images that do not contain appreciable calibration-induced artifacts. This is due to a different scaling factor in the calibration procedure. Goal of this paper is to provide a thorough comparison of advanced classical DPCM and LUT-based methods both on the 1997 and the 2006 AVIRIS datasets. In the 2006 data set, both calibrated data (radiances) and raw data (digital counts) have been compressed. Results strengthen the conclusion that even the most developed LUT-based methods do not show improvements over the state of the art when calibration induced artifacts are missing. Concerning classical DPCMs, the methods based on a classified spectral prediction, whose idea was originally developed by the authors in 2001, provide the best compression results.